Comparison of Adaptive Neuro-Fuzzy based PSS and SSSC Controllers for Enhancing Power System Oscillation Damping
|
|
- Christopher Warren
- 6 years ago
- Views:
Transcription
1 AMSE JOURNALS 216-Series: Advances C; Vol. 71; N 1 ; pp Submitted Dec. 215; Revised Feb. 17, 216; Accepted March 15, 216 Comparison of Adaptive Neuro-Fuzzy based PSS and SSSC Controllers for Enhancing Power System Oscillation Damping D. Murali Assistant Professor, Department of Electrical & Electronics Engineering Government College of Engineering, Salem Tamil Nadu, India (muraliems@gmail.com) Abstract The low frequency electromechanical oscillations have been observed in many power systems and have resulted in system separation on several occasions. The main objective of this paper is to damp out the power angle oscillations of a two-area power system using Power System Stabilizers (PSSs) and Static Synchronous Series Compensator (SSSC) controllers. In this paper, the damping performances of conventional and Adaptive Neuro-Fuzzy Inference System (ANFIS) based PSS and SSSC controllers are compared. Digital simulations of the two-area power system are carried out in Matlab/Simulink environment to validate the efficiency of the proposed ANFIS approach. The results reveal that the ANFIS based SSSC controllers show slightly improved damping performance characteristics compared to ANFIS based PSS controllers. Key words ANFIS, Damping performance, Low frequency electromechanical oscillations, PSS, SSSC, Two-area power system. 1. Introduction With the increasing complexity of the interconnected power networks, the problems on the potential power oscillations, which have the nervous damage against the system stability and the security operation, have been drawn more and more attention as reported by Kundur (1994), Anderson and Fouad (23), Bikash Pal and Balarko Chaudhuri (25). Various theories and technologies were introduced against such power oscillations, such as wide area measurement systems reported by Ray and Venayagamoorthy (28), Flexible AC Transmission System 24
2 (FACTS) devices reported by Zhang et al (26), robust controllers and the design technologies reported by De Oliveira et al (27), and so on, were introduced to enhance the stability and security operating ability of the closed-loop systems. The power system oscillations were first observed as soon as synchronous generators were interconnected to provide more generation capacity and more reliability to power system. Power System Stabilizers (PSSs), which are the excitation system based damping controllers, were then widely used to add damping torque and increase the damping of these oscillations. Kundur et al (1989) provided the analytical work and systematic method to determine PSS parameters for large power generation in a practical power system. The basic PSS design idea in this paper is based on the stabilizer proposed by Demello and Concordia (1969). However, the phase characteristics were obtained using a multi-machine eigenvalue program instead of a single machine model. This work emphasized enhancement of overall system stability, and the authors considered simultaneous damping of inter-area and local modes and discussed the performance of the PSS under different system conditions. In addition to small signal stability performance, the authors also tested the transient stability performance of the PSS and the performance during system islanding. Malik is another person who has done a lot of work in designing fuzzy logic based and neural network based PSS. Hariri and Malik (1996) proposed a fuzzy logic based PSS; the parameters of their PSS were trained off-line. The training was performed over a wide range of conditions for the generating unit and a wide spectrum of possible disturbances was used for the training. Shamsollahi and Malik (1997) also proposed a neural adaptive PSS. In their work, the adaptive neural identifier was first trained offline before being used in the final configuration. Further training of the adaptive neural controller was carried out in every sampling period employing the on-line version of the back propagation method. They applied this neural adaptive PSS both in a SMIB system and a 5-machine system (Shamsollahi and Malik 1999). Also, they investigated the coordination of CPSS and the proposed PSS, and the self-coordination ability of the proposed PSS by simulation. It was shown that the proposed PSS not only provides better damping than CPSS, but also coordinates itself with existing PSS already installed in the system due to its on-line learning ability. Barton (24) presented a robust artificially intelligent Adaptive Neuro-Fuzzy Inference System (ANFIS) based PSS design for damping electromechanical modes of oscillations and enhancing power system synchronous stability. An actual power system was decomposed into separate subsystems; each subsystem consisted of one machine. The local ANFIS based PSS was 25
3 associated with each subsystem. The local feedback controllers were depending only on information particular to their subsystem. The input signals were the speed, power angle and real power output. Nonlinear simulations showed the robustness of the ANFIS based PSS. For the design of FACTS based damping controllers, various approaches were also proposed. A comparative performance of ANFIS based Static synchronous series compensator (SSSC), STATCOM and Unified Power Flow Controller (UPFC) devices for transient stability improvement was given by Ali Shishebori et al (21). Murali and Rajaram (21) explained the effects of STATCOM and SSSC controllers on damping of SMIB system oscillations. Prachanon Kumkratug (211) proposed the control strategy of a SSSC to enlarge the stability region of a simple power system. The proposed nonlinear control of SSSC for damping power system oscillations was investigated through the sample system. Thus, it can be seen from the above reviews that there are many devices (PSS and various FACTS devices) that can help the damping of power system oscillations, and there are also many different control methods for the damping controller design. The objective of this paper is to design advanced PSS and SSSC controllers using the hybrid neuro-fuzzy control approach to enhance damping of power system oscillations. This control approach is concerned with the integration of neural networks and fuzzy technology. The work presented in this paper concentrates on the damping enhancement of a two-area power system by Adaptive Neuro-Fuzzy Inference System (ANFIS) based PSS and SSSC controllers. The structure of the work presented in this paper is organized in the following sequence. A brief review of the literature survey of the related work was presented in the previous paragraphs in the introductory section. Section 2 presents the model of a two-area power system. The architecture of the adaptive neuro-fuzzy inference scheme used in the design of the controller for PSS / SSSC is presented in Section 3. The design of the ANFIS controller is illustrated in Section 4. In Section 5, the simulation results and discussion are given. This is followed by the conclusions in the concluding section. 2. Two-area power system model In the single line diagram of a two-area power system shown in Fig. 1, the bus 3 is taken as reference bus. The system frequency is 5 Hz and the base power is 1 MVA. The frequency of inter-area mode electromechanical oscillations of this system may range from.35 to.75 Hz depending on the operating conditions. The ratings of Generator G1 are 192 MVA and 18 kv and 26
4 that of Generator G2 are 128 MVA and 13.8 kv. The details of the entire system data are given in Yu (1983). Fig. 1. A single line diagram of a Two-area power system Reduced Y- bus Matrix for the above two-area power system is given by Y Bus.846 j2.988 =.287 j j j j j j j j Adaptive Neuro-Fuzzy Inference Scheme (ANFIS) architecture The ANFIS makes use of a hybrid-learning rule to optimize the fuzzy system parameters of a first-order Sugeno system as reported in Jang (1993). The Sugeno fuzzy model (also known as Tsukamoto fuzzy model) was presented to save a systematic method to produce fuzzy rules of a certain input-output data set. The architecture of two inputs, two-rule first-order ANFIS Sugeno system is shown in Fig. 2. The system has only one output. The general ANFIS control structure contains the same components as the FIS (Fuzzy Inference System), except for the neural network block. The structure of the network is composed of a set of units (and connections) arranged into five connected network layers which are described as shown below: 27
5 Fig. 2. An ANFIS architecture for a two-rule Sugeno system The first layer of the ANFIS has adaptive nodes with each node having its function O ( 1 ) for i = 1, 2 or O ) for i = 3, 4 (1) 1, i A x 1, i B ( x 2 where, x 1 and x 2 are the inputs; and Ai and Bi are linguistic labels for the node. And O 1,i is the membership grade of a fuzzy set A ( = A1, A2, B1 or B2) to define the degree of applying the input to the set A. The second layer has fixed nodes, where its output O w x ) ( ) for i = 1, 2. (2) 2, i i A( 1 B x2 The third layer also has fixed nodes with its output O 3, i w i w i w 1 w 2 for i = 1, 2. (3) The nodes of the fourth layer are adaptive nodes, each with a node function w f O 4,i i i wi ( pi x1 qi x2 ri ) (4) where w i is a normalized firing strength produced by layer 3; { p, q, r } is the parameter set of the node, and pointed to consequent parameters. There is a single node in the fifth layer, which is a fixed node, which calculates the resultant output as i i i O 5,i i fi i wi i w w f i i i (5) 28
6 The ANFIS structure is tuned automatically by least square estimation and back propagation algorithm. The algorithm shown above is used in the next section to develop the ANFIS controllers for PSS (and SSSC) to damp out the oscillations in a two-area power system. Because of its flexibility, the ANFIS strategy can be used for a wide range of control applications. 4. ANFIS controller The closed loop block diagram configuration of the system with ANFIS based PSS / SSSC controllers is shown in Fig. 3. To start with, the controller is designed using the ANFIS scheme. Fuzzy logic is one of the successful applications of fuzzy set in which the variables are linguistic rather than the numeric variables. Fuzzy set is an extension of a crisp set where an element can only belong to a set (full membership) or not belong at all (no membership). Fig. 3 Closed loop block diagram showing ANFIS control scheme for PSS / SSSC controllers A fuzzy logic controller is based on a set of fuzzy rules expressed in the form of conditional statements. The basic structure of the developed ANFIS coordination controller for PSS (and SSSC) to damp out the power system oscillations consists of four important parts, viz., fuzzification, knowledge base (rule base), artificial neural network (ANN) and the defuzzification blocks, which are explained in brief in the following paragraphs. The inputs to the ANFIS controller, i.e., the error and the change in error are modeled using the Eqn. (6). e( k) e ( k) e( k) e( k 1) (6) ref r where ref is the reference speed, r is the actual rotor speed, e (k) is the error, and e(k) is the change in error. The fuzzification unit converts the crisp data into linguistic variables, which is given as inputs to the rule based block. The set of 49 rules are written on the basis of previous knowledge/experiences in the rule based block. The rule base block is connected to the neural network block. Back propagation algorithm is used to train the neural network to select the proper set of rule base. For developing the control signal, the training is a very important step in the 29
7 selection of the proper rule base. Once the proper rules are selected and fired, the control signal required to obtain the optimal outputs is generated. The output of the NN unit is given as input to the de-fuzzification unit and the linguistic variables are converted back into the numeric form of data in the crisp form. In the fuzzification process, i.e., in the first stage, the crisp variables, the speed error and the change in error are converted into fuzzy variables or the linguistics variables. The fuzzification maps the 2 input variables to linguistic labels of the fuzzy sets. The fuzzy coordinated controller uses the linguistic labels. Each fuzzy label has an associated membership function. The membership function of Gaussian type used in our work is shown in Fig. 4. The inputs are fuzzified using the fuzzy sets and are given as input to ANFIS controller. The rule base for selection of proper rules using the back propagation algorithm is written as shown in the Table. 1. Fig 4. Gaussian membership functions Table 1. Rules extracted from the conventional PSS (or SSSC) controller Speed Acceleration Dev. NB NM NS ZE PS PM PB NB NB NB NB NB NM NS ZE NM NB NB NM NM NS ZE PS NS NB NM NS NS ZE PS PM ZE NM NM NS ZE ZE PM PM PS NM NS ZE ZE PS PM PB PM NS ZE PS PM PM PM PB PB ZE ZE PM PS PB PB PB The developed fuzzy rules (7 7) are included in the ANFIS controller. The control decisions are made based on the fuzzified variables as shown in the Table 1. The inference involves a set of rules for determining the output decisions. As there are 2 input variables and 7 fuzzified variables, the controller has a set of 49 rules for the ANFIS controller. Out of these 49 rules, the proper rules are selected by the training of the neural network with the help of back propagation algorithm and these selected rules are fired. Further, it has to be converted into numerical output, 3
8 i.e., they have to be de-fuzzified. This process is what is called as de-fuzzification, which is the process of producing a quantifiable result in fuzzy logic. The defuzzifcation transforms fuzzy set information into numeric data information. There are so many methods to perform the defuzzifcation, viz., centre of gravity method, centre of singleton method, maximum methods, the marginal properties of the centroid methods and so on. In this work, the centre of gravity method is used. The output of the defuzzification unit will generate the control commands which in turn are given as input (called as the crisp input) to the PSS (or SSSC) controller. If there is any deviation in the controlled output (crisp output), this is fed back and compared with the set value and the error signal is generated which is given as input to the ANFIS controller which in turn brings back the output to the normal value, thus maintaining stability in the system. Finally, the controlled output is the weighted average of the proper rule based outputs, which are selected by the back propagation algorithm. 5. Simulation results and discussion Initially, the two-area power system model shown in Fig. 1 is installed with conventional PSS controllers, one for the Generator G1 (Area 1) and another one for the Generator G2 (Area 2). The per unit inertia constants of G1 and G2 are taken as 6.4 and 3.1 respectively. The details of the system data are given in Yu (1987). To analyze the damping performance of the PSS controllers, the system is simulated in Matlab/Simulink environment for three different cases such as (i). Without conventional PSS controllers, (ii). With conventional PSS controllers, and (iii). With Adaptive Neuro-Fuzzy based PSS controllers. For simulation, the following set of operating point is considered. Total real power of load P =.8 p.u, Total reactive power of load Q = ±.9 p.u, Terminal voltage Vt = 1.5 p.u, Torque disturbance Tm =.5 p.u, Disturbance clearing time = 5 sec. When the system is simulated without conventional PSS controllers, it is understood from the Fig. 5 that the power angle response for both Area 1 and Area 2 shows increased amplitudes of oscillations and settling times. But, when the same system is simulated with conventional PSS controllers, the power angle oscillations are having decreased amplitudes and they settle down quickly compared to that of the system without conventional PSS controllers. However, the damping performance of the system can be further enhanced by employing Adaptive Neuro- Fuzzy approach to conventional PSS controllers as reported in Murali and Rajaram (213). It is evident from the Fig. 5, Fig. 7, Fig. 9, and Fig. 11 that the system gives almost oscillation-free quickly settled power angle response for both Area 1 and Area 2 when simulated with Adaptive 31
9 Neuro-Fuzzy based PSS controllers. The power angle curves are also shown for negative value of Q. Next, the two-area power system shown in Fig.1 is now installed with two sets of conventional SSSC based damping controllers; one installed between bus 5 and 7, and another between bus 6 and 9 respectively. The system is then simulated in Matlab/Simulink platform for the three different cases such as (i). Without conventional SSSC controllers, (ii). With conventional SSSC controllers, and (iii). With Adaptive Neuro-Fuzzy based SSSC controllers. The same set of operating point is considered as for the system with PSS controllers. When the system is simulated without conventional SSSC controllers, there are large amplitudes of oscillations and increased settling time for oscillations observed in the power angle response as shown in Fig. 6. But, very low amplitudes of power angle oscillations and quick settling times are observed for the system with conventional SSSC controllers as compared to the system response with conventional PSS controllers. The damping performance of the system can however be further improved by applying Adaptive Neuro-Fuzzy control scheme to conventional SSSC controllers. This improvement in damping performance is evident from the Fig. 6, Fig. 8, Fig. 1, and Fig Area 1 Without PSS With Conv. PSS With Adaptive Neuro-Fuzzy PSS P =.8 pu, Q =.9 pu, Torque disturbance =.5 pu Power angle, (pu) Fig. 5. Power angle response for Area 1 with ANFIS based PSS controllers for positive value of Q 32
10 Area 1 Without SSSC With SSSC With Adaptive Neuro-Fuzzy SSSC P =.8 pu, Q =.9 pu, Torque disturbance =.5 pu Power angle, (pu) Fig. 6. Power angle response for Area 2 with ANFIS based SSSC controllers for positive value of Q Power angle, (pu) 9 x Area 2 Without PSS With Conv. PSS With Adaptive Neuro-Fuzzy PSS P =.8 pu, Q =.9 pu, Torque disturbance =.5 pu Fig. 7. Power angle response for Area 2 with ANFIS based PSS controllers for positive value of Q 33
11 Power angle, (pu) 9 x Area 2 Without SSSC With SSSC With Adaptive Neuro-Fuzzy SSSC P =.8 pu, Q =.9 pu, Torque disturbance =.5 pu Fig. 8. Power angle response for Area 2 with ANFIS based PSS controllers for positive value of Q Area 1 Without PSS With Conv. PSS With Adaotive Neuro-Fuzzy PSS.5 P =.8 pu, Q = -.9 pu, Torque disturbance =.5 pu Power angle, (pu) Fig. 9. Power angle response for Area 1 with ANFIS based PSS controllers for negative value of Q 34
12 Area 1 Without SSSC With SSSC With Adaptive Neuro-Fuzzy SSSC P =.8 pu, Q = -.9 pu, Torque disturbance =.5 pu Power angle, (pu) Fig. 1. Power angle response for Area 1 with ANFIS based SSSC controllers for negative value of Q.12.1 Area 2 Without PSS With Conv. PSS With Adaptive Neuro-Fuzzy PSS Power angle, (pu) P =.8 pu, Q = -.9 pu, Torque disturbance =.5 pu Fig. 11. Power angle response for Area 2 with ANFIS based PSS controllers for negative value of Q 35
13 .12.1 Area 2 Without SSSC With SSSC With Adaptive Neuro-Fuzzy SSSC Power angle, (pu) P =.8 pu, Q = -.9 pu, Torque disturbance =.5 pu Fig. 12. Power angle response for Area 2 with ANFIS based SSSC controllers for negative value of Q From the comparison of above Figs. 5-12, it is illustrated that the Adaptive Neuro-Fuzzy based SSSC control scheme can provide somewhat better power oscillation damping performance in terms of slightly reduced amplitudes and settling time when compared with the Adaptive Neuro-Fuzzy based PSS control scheme. 6. Conclusions In this paper, the power system damping enhancement of a two-area power system has been discussed. The efficiency and robustness of the proposed ANFIS control approach are evaluated on the two-area power system with PSS and SSSC based damping controllers installed. Simulation studies are carried out in Matlab/Simulink environment separately for PSS and SSSC controllers. From the time domain simulation results shown in Figs. 5-12, it is inferred that the ANFIS based SSSC controllers are able to provide enhanced damping of power angle oscillations of the system. This enhanced damping is realized in terms of slightly reduced amplitudes and settling times of oscillations compared to ANFIS based PSS control scheme. 36
14 References 1. P. Kundur, Power system stability and control, McGraw Hill, New York, USA, P. M. Anderson, A. A. Fouad, Power system control and stability, Wiley-IEEE Press, Bikash Pal, Balarko Chaudhuri, Robust control in power systems, Springer US Publisher, S. Ray, G.K. Venayagamoorthy, Real time implementation of a measurement-based adaptive wide area control system considering communication delays, IET Generation, Transmission, Distribution, vol. 2, no. 1, pp. 62-7, X. P. Zhang, C. Rehtanz, B. Pal, Flexible AC transmission systems: modeling and control, Springer, Germany, R. De Oliveira, R. Ramos, N. Bretas, A mixed procedure based on classical and modern control to design robust damping controllers, IEEE Transactions on Power Systems, vol. 22, no. 3, pp , P. Kundur, M. Klein, G. J. Rogers, M. S. Zywno, Application of power system stabilizers for enhancement of overall system stability, IEEE Transactions on Power Systems, vol. 4, no. 2, pp , F. P. Demello, C. Concordia, Concepts of synchronous machine stability as affected by excitation control, IEEE Transactions on Power Apparatus and Systems, vol. 88, no. 4, pp , A. Hariri, O. P. Malik, A fuzzy Logic based power system stabilizer with learning ability, IEEE Transactions on Energy Conversion, vol. 11, no. 4, pp , P. Shamsollahi, O. P. Malik, An adaptive power system stabilizer using on-line trained neural networks, IEEE Transactions on Energy Conversion, vol. 12, no. 4, pp , P. Shamsollahi, O. P. Malik, Application of neural adaptive power system stabilizer in a multi-machine power system, IEEE Transactions on Energy Conversion, vol. 14, no. 3, pp , Z. Barton, Robust control in a multimachine power system using adaptive neuro-fuzzy stabilizers, IEE Proceedings-Generation, Transmission and Distribution, vol. 151, no. 2, pp , Ali Shishebori, Forough Taki, Saeed Abazari and Gholamreza Arab Markadeh, Comparison of ANFIS based SSSC, STATCOM and UPFC controllers for transient stability improvement, Majlesi Journal of Electrical Engineering, vol. 4, no. 3, pp ,
15 14. D. Murali, M. Rajaram, The effects of static synchronous compensator and static synchronous series compensator on damping oscillations in power systems, AMSE Journal Modeling, Measurement and Control Series A: General Physics and Electrical Applications, vol. 83, no. 1, pp , Prechanon Kumkratug, Nonlinear control design of series FACTS devices for damping power system oscillation, American Journal of Applied Sciences, vol. 8, no. 2, pp , Y. N. Yu, Electric power system dynamics, Academic Press, J. S. R. Jang, ANFIS: Adaptive-Network-based Fuzzy Inference System, IEEE Transactions on Systems, Man and Cybernetics, vol. 23, no. 3, pp , D. Murali, M. Rajaram, Comparison of damping performance of conventional and neurofuzzy based power system stabilizers applied in multi-machine power systems, Journal of Electrical Engineering Elektrotechnicky Casopis, vol. 64, no. 6, pp ,
DESIGNING POWER SYSTEM STABILIZER FOR MULTIMACHINE POWER SYSTEM USING NEURO-FUZZY ALGORITHM
DESIGNING POWER SYSTEM STABILIZER FOR MULTIMACHINE POWER SYSTEM 55 Jurnal Teknologi, 35(D) Dis. 2001: 55 64 Universiti Teknologi Malaysia DESIGNING POWER SYSTEM STABILIZER FOR MULTIMACHINE POWER SYSTEM
CHAPTER 6 NEURO-FUZZY CONTROL OF TWO-STAGE KY BOOST CONVERTER
73 CHAPTER 6 NEURO-FUZZY CONTROL OF TWO-STAGE KY BOOST CONVERTER 6.1 INTRODUCTION TO NEURO-FUZZY CONTROL The block diagram in Figure 6.1 shows the Neuro-Fuzzy controlling technique employed to control
CHAPTER 6 ANFIS BASED NEURO-FUZZY CONTROLLER
143 CHAPTER 6 ANFIS BASED NEURO-FUZZY CONTROLLER 6.1 INTRODUCTION The quality of generated electricity in power system is dependent on the system output, which has to be of constant frequency and must
Design of Power System Stabilizer using Intelligent Controller
Design of Power System Stabilizer using Intelligent Controller B. Giridharan 1. Dr. P. Renuga 2 M.E.Power Systems Engineering, Associate professor, Department of Electrical &Electronics Engineering, Department
A.V.Sudhakara Reddy 1, M. Ramasekhara Reddy 2, Dr. M. Vijaya Kumar 3
Stability Improvement During Damping of Low Frequency Oscillations with Fuzzy Logic Controller A.V.Sudhakara Reddy 1, M. Ramasekhara Reddy 2, Dr. M. Vijaya Kumar 3 1 (M. Tech, Department of Electrical
Comparison of Fuzzy Logic Based and Conventional Power System Stabilizer for Damping of Power System Oscillations
Comparison of Fuzzy Logic Based and Conventional Power System Stabilizer for Damping of Power System Oscillations K. Prasertwong, and N. Mithulananthan Abstract This paper presents some interesting simulation
Performance Improvement Of AGC By ANFIS
ISSN (Online) : 2319-8753 ISSN (Print) : 2347-6710 International Journal of Innovative Research in Science, Engineering and Technology Volume 3, Special Issue 3, March 2014 2014 International Conference
CHAPTER 4 AN EFFICIENT ANFIS BASED SELF TUNING OF PI CONTROLLER FOR CURRENT HARMONIC MITIGATION
92 CHAPTER 4 AN EFFICIENT ANFIS BASED SELF TUNING OF PI CONTROLLER FOR CURRENT HARMONIC MITIGATION 4.1 OVERVIEW OF PI CONTROLLER Proportional Integral (PI) controllers have been developed due to the unique
Simulation of Synchronous Machine in Stability Study for Power System: Garri Station as a Case Study
Simulation of Synchronous Machine in Stability Study for Power System: Garri Station as a Case Study Bahar A. Elmahi. Industrial Research & Consultancy Center, baharelmahi@yahoo.com Abstract- This paper
INTERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY (IJEET)
INTERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY (IJEET) ISSN 0976 6545(Print) ISSN 0976 6553(Online) Volume 3, Issue 1, January- June (2012), pp. 226-234 IAEME: www.iaeme.com/ijeet.html Journal
Voltage Control and Power System Stability Enhancement using UPFC
International Conference on Renewable Energies and Power Quality (ICREPQ 14) Cordoba (Spain), 8 th to 10 th April, 2014 Renewable Energy and Power Quality Journal (RE&PQJ) ISSN 2172-038 X, No.12, April
Fuzzy Control Scheme for Damping of Oscillations in Multi Machine. Power System with UPFC
Fuzzy Control Scheme for Damping of Oscillations in Multi Machine Power System with UPFC Aparna Kumari 1, Anjana Tripathi 2, Shashi Kala Kumari 3 1 MTech Scholar, Department of Electrical Engineering,
A Novel PSS Design for Single Machine Infinite Bus System Based on Artificial Bee Colony
A Novel PSS Design for Single Machine Infinite Bus System Based on Artificial Bee Colony Prof. MS Jhamad*, Surbhi Shrivastava** *Department of EEE, Chhattisgarh Swami Vivekananda Technical University,
A Real-Time Platform for Teaching Power System Control Design
A Real-Time Platform for Teaching Power System Control Design G. Jackson, U.D. Annakkage, A. M. Gole, D. Lowe, and M.P. McShane Abstract This paper describes the development of a real-time digital simulation
Tuning Of Conventional Pid And Fuzzy Logic Controller Using Different Defuzzification Techniques
Tuning Of Conventional Pid And Fuzzy Logic Controller Using Different Defuzzification Techniques Afshan Ilyas, Shagufta Jahan, Mohammad Ayyub Abstract:- This paper presents a method for tuning of conventional
Automatic Generation Control of Two Area using Fuzzy Logic Controller
Automatic Generation Control of Two Area using Fuzzy Logic Yagnita P. Parmar 1, Pimal R. Gandhi 2 1 Student, Department of electrical engineering, Sardar vallbhbhai patel institute of technology, Vasad,
Application of Fuzzy Logic Controller in Shunt Active Power Filter
IJIRST International Journal for Innovative Research in Science & Technology Volume 2 Issue 11 April 2016 ISSN (online): 2349-6010 Application of Fuzzy Logic Controller in Shunt Active Power Filter Ketan
Design of an Intelligent Pressure Control System Based on the Fuzzy Self-tuning PID Controller
Design of an Intelligent Pressure Control System Based on the Fuzzy Self-tuning PID Controller 1 Deepa S. Bhandare, 2 N. R.Kulkarni 1,2 Department of Electrical Engineering, Modern College of Engineering,
Transient Stability Improvement Of IEEE 9 Bus System With Shunt FACTS Device STATCOM
Transient Stability Improvement Of IEEE 9 Bus System With Shunt FACTS Device STATCOM P.P. Panchbhai 1, P.S.Vaidya 2 1Pratiksha P Panchbhai, Dept. of Electrical Engineering, G H Raisoni College of Engineering
Modelling of Fuzzy Generic Power System Stabilizer for SMIB System
Modelling of Fuzzy Generic Power System Stabilizer for SMIB System D.Jasmitha 1, Dr.R.Vijayasanthi 2 PG Student, Dept. of EEE, Andhra University (A), Visakhapatnam, India 1 Assistant Professor, Dept. of
A new fuzzy self-tuning PD load frequency controller for micro-hydropower system
IOP Conference Series: Earth and Environmental Science PAPER OPEN ACCESS A new fuzzy self-tuning PD load frequency controller for micro-hydropower system Related content - A micro-hydropower system model
PERFORMANCE COMPARISON OF POWER SYSTEM STABILIZER WITH AND WITHOUT FACTS DEVICE
PERFORMANCE COMPARISON OF POWER SYSTEM STABILIZER WITH AND WITHOUT FACTS DEVICE Amit Kumar Vidyarthi 1, Subrahmanyam Tanala 2, Ashish Dhar Diwan 1 1 M.Tech Scholar, 2 Asst. Prof. Dept. of Electrical Engg.,
Abstract: PWM Inverters need an internal current feedback loop to maintain desired
CURRENT REGULATION OF PWM INVERTER USING STATIONARY FRAME REGULATOR B. JUSTUS RABI and Dr.R. ARUMUGAM, Head of the Department of Electrical and Electronics Engineering, Anna University, Chennai 600 025.
Comparison of FACTS Devices for Power System Stability Enhancement
Comparison of FACTS Devices for Power System Stability Enhancement D. Murali Research Scholar in EEE Dept., Government College of Engineering, Bargur-635 104, Tamilnadu, India. Dr. M. Rajaram Professor
EXPERIMENTAL INVESTIGATION OF THE ROLE OF STABILIZERS IN THE ENHANCEMENT OF AUTOMATIC VOLTAGE REGULATORS PERFORMANCE
Engineering Journal of Qatar University, Vol. 4, 1991, p. 91-102. EXPERIMENTAL INVESTIGATION OF THE ROLE OF STABILIZERS IN THE ENHANCEMENT OF AUTOMATIC VOLTAGE REGULATORS PERFORMANCE K. I. Saleh* and M.
DC Motor Speed Control: A Case between PID Controller and Fuzzy Logic Controller
DC Motor Speed Control: A Case between PID Controller and Fuzzy Logic Controller Philip A. Adewuyi Mechatronics Engineering Option, Department of Mechanical and Biomedical Engineering, Bells University
FUZZY AND NEURO-FUZZY MODELLING AND CONTROL OF NONLINEAR SYSTEMS
FUZZY AND NEURO-FUZZY MODELLING AND CONTROL OF NONLINEAR SYSTEMS Mohanadas K P Department of Electrical and Electronics Engg Cukurova University Adana, Turkey Shaik Karimulla Department of Electrical Engineering
Arvind Pahade and Nitin Saxena Department of Electrical Engineering, Jabalpur Engineering College, Jabalpur, (MP), India
e t International Journal on Emerging Technologies 4(1): 10-16(2013) ISSN No. (Print) : 0975-8364 ISSN No. (Online) : 2249-3255 Control of Synchronous Generator Excitation and Rotor Angle Stability by
ANFIS based 48-Pulse STATCOM Controller for Enhancement of Power System Stability
ANFIS based 48-Pulse STATCOM Controller for Enhancement of Power System Stility Subir Datta and Anjan Kumar Roy Abstract The paper presents a new ANFIS-based controller for enhancement of voltage stility
Different Types of Inference Method for Fuzzy Power System Stabilizer Analysis
VOL. 6, NO. 3, 27, 7-22 www.fke.utm.my/elektrika ISSN 28-4428 Different Types of Inference Method for Fuzzy Power System Stabilizer Analysis Nur Safura Ab Khalid *, Mohd Wazir Mustafa and Rasyidah Mohamad
OPTIMAL TORQUE RIPPLE CONTROL OF ASYNCHRONOUS DRIVE USING INTELLIGENT CONTROLLERS
OPTIMAL TORQUE RIPPLE CONTROL OF ASYNCHRONOUS DRIE USING INTELLIGENT CONTROLLERS J.N.Chandra Sekhar 1 and Dr.G. Marutheswar 2 1 Department of EEE, Assistant Professor, S University College of Engineering,
Transient stability improvement by using shunt FACT device (STATCOM) with Reference Voltage Compensation (RVC) control scheme
I J E E E C International Journal of Electrical, Electronics ISSN No. (Online) : 2277-2626 and Computer Engineering 2(1): 7-12(2013) Transient stability improvement by using shunt FACT device (STATCOM)
LOW FREQUENCY OSCILLATION DAMPING BY DISTRIBUTED POWER FLOW CONTROLLER WITH A ROBUST FUZZY SUPPLEMENTARY CONTROLLER
LOW FREQUENCY OSCILLATION DAMPING BY DISTRIBUTED POWER FLOW CONTROLLER WITH A ROBUST FUZZY SUPPLEMENTARY CONTROLLER C. Narendra Raju 1, V.Naveen 2 1PG Scholar, Department of EEE, JNTU Anantapur, Andhra
A PLC-based Self-tuning PI-Fuzzy Controller for Linear and Non-linear Drives Control
A PLC-based Self-tuning PI-Fuzzy Controller for Linear and Non-linear Drives Control Muhammad Arrofiq *1, Nordin Saad *2 Universiti Teknologi PETRONAS Tronoh, Perak, Malaysia muhammad_arrofiq@utp.edu.my
EVALUATION OF A NEW MODEL FOR UPFC OPERATING AS IMPEDANCE COMPENSATION APPLIED TO MULTI- MACHINE SYSTEMS WITH NONLINEAR LOAD
Journal of Engineering Science and Technology ol. 9, No. 6 (04) 678-689 School of Engineering, Taylor s University EALUATION OF A NEW MODEL FOR UPFC OPERATING AS IMPEDANCE COMPENSATION APPLIED TO MULTI-
Damping Power system Oscillation using Static Synchronous Series Compensator (SSSC)
Damping Power system Oscillation using Static Synchronous Series Compensator (SSSC) Girish Kumar Prasad 1, Dr. Malaya S Dash 2 1M-Tech Scholar, Dept. of Electrical & Electronics Engineering, Technocrats
Performance Analysis of PSO Optimized Fuzzy PI/PID Controller for a Interconnected Power System
Performance Analysis of PSO Optimized Fuzzy PI/PID Controller for a Interconnected Power System 1 Pogiri Ramu, Anusha M 2, Gayatri B 3 and *Halini Samalla 4 Department of Electrical & Electronics Engineering
High Efficiency DC/DC Buck-Boost Converters for High Power DC System Using Adaptive Control
American-Eurasian Journal of Scientific Research 11 (5): 381-389, 2016 ISSN 1818-6785 IDOSI Publications, 2016 DOI: 10.5829/idosi.aejsr.2016.11.5.22957 High Efficiency DC/DC Buck-Boost Converters for High
Transient Stability Improvement of Multi Machine Power Systems using Matrix Converter Based UPFC with ANN
IJSRD - International Journal for Scientific Research & Development Vol. 3, Issue 04, 2015 ISSN (online): 2321-0613 Transient Stability Improvement of Multi Machine Power Systems using Matrix Converter
Modeling & Simulation of PMSM Drives with Fuzzy Logic Controller
Vol. 3, Issue. 4, Jul - Aug. 2013 pp-2492-2497 ISSN: 2249-6645 Modeling & Simulation of PMSM Drives with Fuzzy Logic Controller Praveen Kumar 1, Anurag Singh Tomer 2 1 (ME Scholar, Department of Electrical
CHAPTER 4 LOAD FREQUENCY CONTROL OF INTERCONNECTED HYDRO-THERMAL SYSTEM
53 CHAPTER 4 LOAD FREQUENCY CONTROL OF INTERCONNECTED HYDRO-THERMAL SYSTEM 4.1 INTRODUCTION Reliable power delivery can be achieved through interconnection of hydro and thermal system. In recent years,
Comparative analysis of Conventional MSSMC and Fuzzy based MSSMC controller for Induction Motor
American International Journal of Research in Science, Technology, Engineering & Mathematics Available online at http://www.iasir.net ISSN (Print): 2328-3491, ISSN (Online): 2328-3580, ISSN (CD-ROM): 2328-3629
Comparison between Genetic and Fuzzy Stabilizer and their effect on Single-Machine Power System
J. Basic. Appl. Sci. Res., 1(11)214-221, 211 211, TextRoad Publication ISSN 29-434 Journal of Basic and Applied Scientific Research www.textroad.com Comparison between Genetic and Fuzzy Stabilizer and
Development of Real time controller of a Single Machine Infinite Bus system with PSS
Development of Real time controller of a Single Machine Infinite Bus system with PSS Mrs.Ami T.Patel 1, Mr.Hardik A.Shah 2 Prof.S. K.Shah 3 1 Research Scholar, Electrical Engineering Department: FTE,M.S.University
We are IntechOpen, the world s leading publisher of Open Access books Built by scientists, for scientists. International authors and editors
We are IntechOpen, the world s leading publisher of Open Access books Built by scientists, for scientists 3,9 6, 2M Open access books available International authors and editors Downloads Our authors are
1. Governor with dynamics: Gg(s)= 1 2. Turbine with dynamics: Gt(s) = 1 3. Load and machine with dynamics: Gp(s) = 1
Load Frequency Control of Two Area Power System Using PID and Fuzzy Logic 1 Rajendra Murmu, 2 Sohan Lal Hembram and 3 A.K. Singh 1 Assistant Professor, 2 Reseach Scholar, Associate Professor 1,2,3 Electrical
TWO AREA CONTROL OF AGC USING PI & PID CONTROL BY FUZZY LOGIC
TWO AREA CONTROL OF AGC USING PI & PID CONTROL BY FUZZY LOGIC Puran Lal 1, Mainak Roy 2 1 M-Tech (EL) Student, 2 Assistant Professor, Department of EEE, Lingaya s University, Faridabad, (India) ABSTRACT
Performance Analysis of Transient Stability and Its Improvement Using Fuzzy Logic Based Power System Stabilizer
Performance Analysis of Transient Stability and Its Improvement Using Fuzzy Logic Based Power System Stabilizer Dilip Parmar 1, Amit ved 2 1 M.E. (PG Scholar), 2 Associate professor in Electrical Engineering
PERFORMANCE ANALYSIS OF SVPWM AND FUZZY CONTROLLED HYBRID ACTIVE POWER FILTER
International Journal of Electrical and Electronics Engineering Research (IJEEER) ISSN 2250-155X Vol. 3, Issue 2, Jun 2013, 309-318 TJPRC Pvt. Ltd. PERFORMANCE ANALYSIS OF SVPWM AND FUZZY CONTROLLED HYBRID
Development of a Fuzzy Logic Controller for Industrial Conveyor Systems
American Journal of Science, Engineering and Technology 217; 2(3): 77-82 http://www.sciencepublishinggroup.com/j/ajset doi: 1.11648/j.ajset.21723.11 Development of a Fuzzy Logic Controller for Industrial
OPTIMAL DESIGN OF POWER SYSTEM STABILIZERS CONTROL FOR MONOMACHINE AND MULTIMACHINE USING INTELLIGENT CONTROL TECHNIQUES -FUZZY LOGIC-
OPTIMAL DESIGN OF POWER SYSTEM STABILIZERS CONTROL FOR MONOMACHINE AND MULTIMACHINE USING INTELLIGENT CONTROL TECHNIQUES -FUZZY LOGIC- OUISSAM BELGHAZI, MOULAY RACHID DOUIRI, MOHAMED CHERKAOUI, MOHAMMED
International Journal of Advance Engineering and Research Development
Scientific Journal of Impact Factor (SJIF): 4.72 International Journal of Advance Engineering and Research Development Volume 4, Issue 4, April -2017 e-issn (O): 2348-4470 p-issn (P): 2348-6406 Damping
Design of Fast Real Time Controller for the Dynamic Voltage Restorer Based on Instantaneous Power Theory
International Journal of Energy and Power Engineering 2016; 5(2-1): 1-6 Published online October 10, 2015 (http://www.sciencepublishinggroup.com//epe) doi: 10.11648/.epe.s.2016050201.11 ISSN: 2326-957X
The Pitch Control Algorithm of Wind Turbine Based on Fuzzy Control and PID Control
Energy and Power Engineering, 2013, 5, 6-10 doi:10.4236/epe.2013.53b002 Published Online May 2013 (http://www.scirp.org/journal/epe) The Pitch Control Algorithm of Wind Turbine Based on Fuzzy Control and
Fuzzy logic damping controller for FACTS devices in interconnected power systems. Ni, Yixin; Mak, Lai On; Huang, Zhenyu; Chen, Shousun; Zhang, Baolin
Title Fuzzy logic damping controller for FACTS devices in interconnected power systems Author(s) Citation Ni, Yixin; Mak, Lai On; Huang, Zhenyu; Chen, Shousun; Zhang, Baolin IEEE International Symposium
FUZZY BASED SMART LOAD PRIMARY FREQUENCY CONTROL CONTRIBUTION USING REACTIVE COMPENSATION
FUZZY BASED SMART LOAD PRIMARY FREQUENCY CONTROL CONTRIBUTION USING REACTIVE COMPENSATION G.HARI PRASAD 1, Dr. K.JITHENDRA GOWD 2 1 Student, dept. of Electrical and Electronics Engineering, JNTUA Anantapur,
CHAPTER 4 ON LINE LOAD FREQUENCY CONTROL
CHAPTER 4 ON LINE LOAD FREQUENCY CONTROL The main objective of Automatic Load Frequency Control (LFC) is to maintain the frequency and active power change over lines at their scheduled values. As frequency
A Novel Fuzzy Neural Network Based Distance Relaying Scheme
902 IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 15, NO. 3, JULY 2000 A Novel Fuzzy Neural Network Based Distance Relaying Scheme P. K. Dash, A. K. Pradhan, and G. Panda Abstract This paper presents a new
CHAPTER 4 FUZZY LOGIC CONTROLLER
62 CHAPTER 4 FUZZY LOGIC CONTROLLER 4.1 INTRODUCTION Unlike digital logic, the Fuzzy Logic is a multivalued logic. It deals with approximate perceptive rather than precise. The effective and efficient
Implementation of PSS and STATCOM Controllers for Power System Stability Enhancement
International Journal of Engineering Research and Development e-issn: 78-67X, p-issn: 78-8X, www.ijerd.com Volume 9, Issue 7 (January 4), PP. -8 Implementation of PSS and STATCOM Controllers for Power
EXCITATION SYSTEM MODELS OF GENERATORS OF BALTI AND EESTI POWER PLANTS
Oil Shale, 2007, Vol. 24, No. 2 Special ISSN 0208-189X pp. 285 295 2007 Estonian Academy Publishers EXCITATION SYSTEM MODELS OF GENERATORS OF BALTI AND EESTI POWER PLANTS R. ATTIKAS *, H.TAMMOJA Department
UNIVERSITY OF CALGARY. Adaptive Simplified Neuro-Fuzzy Controller as Supplementary Stabilizer for SVC. Anas M. AlBakkar A THESIS
UNIVERSITY OF CALGARY Adaptive Simplified Neuro-Fuzzy Controller as Supplementary Stabilizer for SVC by Anas M. AlBakkar A THESIS SUBMITTED TO THE FACULTY OF GRADUATE STUDIES IN PARTIAL FULFILMENT OF THE
Steady State Stability Analysis of a CSI Fed Synchronous Motor Drive System with Damper Windings Included using ANFIS
AMSE JOURNALS 2014-Series: ADVANCES C; Vol. 69; N 1; pp 39-57 Submitted October 2013; Revised Dec. 26, 2013; Accepted Feb. 1, 2014 Steady State Stability Analysis of a CSI Fed Synchronous Motor Drive System
ISSN: [IDSTM-18] Impact Factor: 5.164
IJESRT INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY SPEED CONTROL OF DC MOTOR USING FUZZY LOGIC CONTROLLER Pradeep Kumar 1, Ajay Chhillar 2 & Vipin Saini 3 1 Research scholar in
Application Of Power System Stabilizer At Serir Power Plant
Vol. 3 Issue 4, April - 27 Application Of Power System Stabilizer At Serir Power Plant *T. Hussein, **A. Shameh Electrical and Electronics Dept University of Benghazi Benghazi- Libya *Tawfiq.elmenfy@uob.edu.ly
Improvement of Transient stability in Power Systems with Neuro- Fuzzy UPFC
American Journal of Engineering Research (AJER) e-issn : 2320-0847 p-issn : 2320-0936 Volume-02, Issue-11, pp-48-60 www.ajer.org Research Paper Open Access Improvement of Transient stability in Power Systems
International Journal of Scientific & Engineering Research, Volume 7, Issue 12, December-2016 ISSN Ribin MOHEMMED, Abdulkadir CAKIR
International Journal of Scientific & Engineering Research, Volume 7, Issue 12, December-216 1668 Modeling And Simulation Of Differential Relay For Stator Winding Generator Protection By Using ANFIS Algorithm
Investigations of Fuzzy Logic Controller for Sensorless Switched Reluctance Motor Drive
IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE) e-issn: 2278-1676,p-ISSN: 2320-3331, Volume 11, Issue 1 Ver. I (Jan Feb. 2016), PP 30-35 www.iosrjournals.org Investigations of Fuzzy
Optimal Placement of Shunt Connected Facts Device in a Series Compensated Long Transmission Line
Journal of Agriculture and Life Sciences Vol. 1, No. 1; June 2014 Optimal Placement of Shunt Connected Facts Device in a Series Compensated Long Transmission Line Sudhakar. Muthyala EEE Dept. University
LARGE-SCALE WIND POWER INTEGRATION, VOLTAGE STABILITY LIMITS AND MODAL ANALYSIS
LARGE-SCALE WIND POWER INTEGRATION, VOLTAGE STABILITY LIMITS AND MODAL ANALYSIS Giuseppe Di Marzio NTNU giuseppe.di.marzio@elkraft.ntnu.no Olav B. Fosso NTNU olav.fosso@elkraft.ntnu.no Kjetil Uhlen SINTEF
6545(Print), ISSN (Online) Volume 4, Issue 1, January- February (2013), IAEME & TECHNOLOGY (IJEET)
INTERNATIONAL International Journal of JOURNAL Electrical Engineering OF ELECTRICAL and Technology (IJEET), ENGINEERING ISSN 0976 & TECHNOLOGY (IJEET) ISSN 0976 6545(Print) ISSN 0976 6553(Online) Volume
Fuzzy Intelligent Controller for the MPPT of a Photovoltaic Module in comparison with Perturb and Observe algorithm
Fuzzy Intelligent Controller for the MPPT of a Photovoltaic Module in comparison with Perturb and Observe algorithm B. Amarnath Naidu 1, S. Anil Kumar 2 and Dr. M. Siva Sathya Narayana 3 1, 2 Assistant
ADVANCES in NATURAL and APPLIED SCIENCES
ADVANCES in NATURAL and APPLIED SCIENCES ISSN: 1995-0772 Published BYAENSI Publication EISSN: 1998-1090 http://www.aensiweb.com/anas 2017 April 11(4): pages 402-409 Open Access Journal Design and Implementation
Torque Control of BLDC Motor using ANFIS Controller M. Anka Rao 1 M. Vijaya kumar 2 H. Jagadeeswara Rao 3
IJSRD - International Journal for Scientific Research & Development Vol. 3, Issue 08, 2015 ISSN (online): 2321-0613 Torque Control of BLDC Motor using ANFIS Controller M. Anka Rao 1 M. Vijaya kumar 2 H.
Development of Dynamic Test Cases in OPAL-RT Real-time Power System Simulator
Development of Dynamic Test Cases in OPAL-RT Real-time Power System Simulator Shiv Kumar Singh, Bibhu P. Padhy, Student Member, IEEE, S. Chakrabarti, Senior Member, IEEE, S.N. Singh, Senior Member, IEEE,
Intelligent Temperature Controller for Water- Bath System Om Prakash Verma, Rajesh Singla, Rajesh Kumar
Intelligent Temperature Controller for Water- Bath System Om Prakash Verma, Rajesh Singla, Rajesh Kumar International Science Index, Electrical and Computer Engineering waset.org/publication/17300 Abstract
International Journal of Scientific & Engineering Research, Volume 6, Issue 6, June-2015 ISSN
ISSN 2229-5518 359 Automatic Generation Control in Three Area Interconnected Power System of Thermal Generating Unit using Evolutionary Controller Ashish Dhamanda 1, A.K.Bhardwaj 2 12 Department of Electrical
Reduction of Voltage Imbalance in a Two Feeder Distribution System Using Iupqc
International Journal of Engineering Research and Development e-issn: 2278-067X, p-issn: 2278-800X, www.ijerd.com Volume 10, Issue 7 (July 2014), PP.01-15 Reduction of Voltage Imbalance in a Two Feeder
SVM-DTC OF AN INDUCTION MOTOR BASED ON VOLTAGE AND STATOR FLUX ANGLE USING FUZZY LOGIC CONTROLLER
SVM-DTC OF AN INDUCTION MOTOR BASED ON VOLTAGE AND STATOR FLUX ANGLE USING FUZZY LOGIC CONTROLLER T.Sravani 1, S.Sridhar 2 1PG Student(Power & Industrial Drives), Department of EEE, JNTU Anantapuramu,
DESIGN OF A MODE DECOUPLING FOR VOLTAGE CONTROL OF WIND-DRIVEN IG SYSTEM
IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE) e-issn: 2278-1676,p-ISSN: 2320-3331, Volume 8, Issue 5 (Nov. - Dec. 2013), PP 41-45 DESIGN OF A MODE DECOUPLING FOR VOLTAGE CONTROL OF
DEVELOPMENT OF NEURO-FUZZY CONTROLLER FOR A TWO TERMINAL HVDC LINK
PARITANTRA Vol. 9 No. JUNE 4 DEVELOPMENT OF NEURO-FUZZY CONTROLLER FOR A TWO TERMINAL HVDC LINK Kanungo Barada Mohanty Department of Electrical Engineering National Institute of Technology Rourkela-7698
Static Synchronous Compensator (STATCOM) for the improvement of the Electrical System performance with Non Linear load 1
Static Synchronous Compensator (STATCOM) for the improvement of the Electrical System performance with Non Linear load MADHYAMA V. WANKHEDE Department Of Electrical Engineering G. H. Raisoni College of
A Comparative Study of P-I, I-P, Fuzzy and Neuro-Fuzzy Controllers for Speed Control of DC Motor Drive
International Journal of Electrical Systems Science and Engineering : 9 A Comparative Study of PI, IP, Fuzzy and NeuroFuzzy Controllers for Speed Control of DC Motor Drive S.R. Khuntia, K.B. Mohanty, S.
IJESRT. Scientific Journal Impact Factor: (ISRA), Impact Factor: 1.852
IJESRT INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY Design of Self-tuning PID controller using Fuzzy Logic for Level Process P D Aditya Karthik *1, J Supriyanka 2 *1, 2 Department
ELEMENTS OF FACTS CONTROLLERS
1 ELEMENTS OF FACTS CONTROLLERS Rajiv K. Varma Associate Professor Hydro One Chair in Power Systems Engineering University of Western Ontario London, ON, CANADA rkvarma@uwo.ca POWER SYSTEMS - Where are
Intelligent Fuzzy-PID Hybrid Control for Temperature of NH3 in Atomization Furnace
289 Intelligent Fuzzy-PID Hybrid Control for Temperature of NH3 in Atomization Furnace Assistant Professor, Department of Electrical Engineering B.H.S.B.I.E.T. Lehragaga Punjab technical University Jalandhar
Fuzzy Controllers for Boost DC-DC Converters
IOSR Journal of Electronics and Communication Engineering (IOSR-JECE) e-issn: 2278-2834,p- ISSN: 2278-8735 PP 12-19 www.iosrjournals.org Fuzzy Controllers for Boost DC-DC Converters Neethu Raj.R 1, Dr.
Optimal PSS Tuning by using Artificial Bee Colony
Journal of Novel Applied Sciences Available online at www.jnasci.org 2013 JNAS Journal-2013-2-10/534-540 ISSN 2322-5149 2013 JNAS Optimal PSS Tuning by using Artificial Bee Colony Mostafa Abdollahi *,
Robust controller design for LFO damping
International society of academic and industrial research www.isair.org IJARAS International Journal of Academic Research in Applied Science 1(4): 1-8, 2012 ijaras.isair.org Robust controller design for
Sugeno Type Fuzzy-PID Hybrid Controller for Efficient Inter-area Power Oscillation Damping in Two Area Four Machine Power System by using MATLAB
Sugeno Type Fuzzy-PID Hybrid Controller for Efficient Inter-area Power Oscillation Damping in Two Area Four Machine Power System by using MATLAB 1 Sundeep Pradeep Kashyap, 2 Mr. Mahesh Singh, 3 Dr. R.N.
Locating Earth Fault of Synchronous Generator using Wavelet Transform and ANFIS
49, Issue 1 (2018) 1-6 Journal of Advanced Research Design Journal homepage: www.akademiabaru.com/ard.html ISSN: 2289-7984 Locating Earth Fault of Synchronous Generator using Wavelet Transform and ANFIS
Power Quality Enhancement and Mitigation of Voltage Sag using DPFC
Power Quality Enhancement and Mitigation of Voltage Sag using DPFC M. Bindu Sahithi 1, Y. Vishnu Murthulu 2 1 (EEE Department, Prasad V Potluri Siddhartha Institute of Technology, A.p, India) 2 (Assistant
Design and Simulation of Fuzzy Logic controller for DSTATCOM In Power System
Design and Simulation of Fuzzy Logic controller for DSTATCOM In Power System Anju Gupta Department of Electrical and Electronics Engg. YMCA University of Science and Technology anjugupta112@gmail.com P.
Control Of Three Phase BLDC Motor Using Fuzzy Logic Controller Anjali. A. R M-Tech in Powerelectronics & Drives,Calicut University
Control Of Three Phase BLDC Motor Using Fuzzy Logic Controller Anjali. A. R M-Tech in Powerelectronics & Drives,Calicut University Abstract Brushless DC (BLDC) motor drives are becoming widely used in
International Journal of Advance Engineering and Research Development. Fuzzy Logic Based Automatic Generation Control of Interconnected Power System
Scientific Journal of Impact Factor (SJIF): 3.134 International Journal of Advance Engineering and Research Development Volume 3, Issue 1, January -2016 e-issn (O): 2348-4470 p-issn (P): 2348-6406 Fuzzy
Control of DC-DC Buck Boost Converter Output Voltage Using Fuzzy Logic Controller
International Journal of Control Theory and Applications ISSN : 0974-5572 International Science Press Volume 10 Number 25 2017 Control of DC-DC Buck Boost Converter Output Voltage Using Fuzzy Logic Controller
Dynamic stability of power systems
Dynamic stability of power systems Dr Rafael Segundo Research Associate Zurich University of Applied Science segu@zhaw.ch SCCER School- Shaping the Energy Transition Engelberg, 20 October 2017 Agenda Fundamentals
Power System Stability and Optimization Techniques: An Overview
RESEARCH ARTICLE OPEN ACCESS Power System Stability and Optimization Techniques: An Overview Monika 1, Balwinder Singh 2, Rintu Khanna 3 1 Research Scholar, PEC University of Technology,Chandigarh, goelmonika545@gmail.com
Keywords STATCOM, Multimachine, Fuzzy Logic Controller, 9-Bus System, Reactive Power Control
Volume 6, Issue 12, December 2016 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Reactive Power
Application of Fuzzy Logic Controller in UPFC to Mitigate THD in Power System
International Journal of Engineering Research and Development e-issn: 2278-067X, p-issn: 2278-800X, www.ijerd.com Volume 9, Issue 8 (January 2014), PP. 25-33 Application of Fuzzy Logic Controller in UPFC