Volume/Weight/Cost Comparison of a 1MVA 10 kV/400V Solid-State against a Conventional Low-Frequency Distribution Transformer

ABSTRACT:  

Solid-State Transformers (SSTs) are an emergent topic in the context of the Smart Grid paradigm, where SSTs could replace conventional passive transformers to add flexibility and controllability, such as power routing capabilities or reactive power compensation, to the grid. This paper presents a comparison of a 1000 kVA three-phase, low-frequency distribution transformer (LFT) and an equally rated SST, with respect to volume, weight, losses, and material costs, where the corresponding data of the SST is partly based on a full-scale prototype design. It is found that the SST’s costs are at least five times and its losses about three times higher, its weight similar but its volume reduced to less than 80 %. In addition, an AC/DC application is also considered, where the comparison turns out in favor of the SST-based concept, since its losses are only about half compared to the LFT-based system, and the volume and the weight are reduced to about one third, whereas the material costs advantage of the LFT is much less pronounced.

KEYWORDS:

1.      Induction Motor (IM)

2.      Indirect Field-Oriented Control (IFOC)

3.       Pulse Width Modulation (PWM)

SOFTWARE: MATLAB/SIMULINK

CIRCUIT DIAGRAM:

Fig. 1. Power circuit of one converter cell used in the SST’s MV side phase

stack

EXPERIMENTAL RESULTS:

Fig. 2. MV side output voltage and resulting line current for (a) the cascaded  1000 kVA MV converter, and (b) corresponding LV side waveforms for one of the 500 kVA LV converter units (cf. Fig. 1(b)) for full-load active power operation.

CONCLUSION:

This paper provides a comparison of a 1000 kVA three-phase LFT and an equally rated SST with respect to material costs, weight, volume and losses. As a direct AC/AC replacement for an LFT, the SST solution realizes benefits with respect to volume, but on the other hand is significantly less efficient and has at least five times higher material costs. However, SST-based solutions can clearly outperform conventional transformers plus LV rectifier systems in modern AC/DC applications, achieving about half the losses and one third of the weight and volume, respectively. All in all, SST technology has significant potential also in grid applications, especially with the Smart Grid being heavily promoted and becoming a reality in the foreseeable future, which increases the requirements in terms of flexibility, intelligence and controllability. However, the usefulness of an SST can only be judged in the context of a given application; there is not a general SST solution that fits every need. Current state-of-the-art LFT technology evolved during more than a hundred years, and represents therefore a truly experienced competitor. Thus SSTs, and explicitly also their relation to various application scenarios, regarding both, technical and economical aspects, should be prominently included in any power electronics or energy research agenda.

REFERENCES:

[1] W. McMurray, “Power converter circuits having a high frequency link,” U.S. Patent 3,581,212, 1970.

[2] S. D. Sudhoff, “Solid state transformer,” U.S. Patent 5,943,229, 1999.

[3] M. Kang, P. Enjeti, and I. Pitel, “Analysis and design of electronic transformers for electric power distribution system,” IEEE Trans. Power Electron., vol. 14, no. 6, pp. 1133–1141, 1999.

[4] M. Manjrekar, R. Kieferndorf, and G. Venkataramanan, “Power electronic transformers for utility applications,” in Conf. Rec. IEEE Industry Applications Conf., Rome, Italy, 2000, pp. 2496–2502.

[5] L. Heinemann and G. Mauthe, “The universal power electronics based distribution transformer, an unified approach,” in Proc. 32nd Annu.  IEEE Power Electronics Specialists Conf. (PESC), Vancouver, Canada, 2001, pp. 504–509.

Fault Detection and Classification In Electrical Power Transmission System Using Artificial Neural Network

ABSTRACT:

This paper focuses on the detection and classification of the faults on electrical power transmission line using artificial neural networks. The three phase currents and voltages of one end are taken as inputs in the proposed scheme. The feed forward neural network along with back propagation algorithm has been employed for detection and classification of the fault for analysis of each of the three phases involved in the process. A detailed analysis with varying number of hidden layers has been performed to validate the choice of the neural network. The simulation results concluded that the present method based on the neural network is efficient in detecting and classifying the faults on transmission lines with satisfactory performances. The different faults are simulated with different parameters to check the versatility of the method. The proposed method can be extended to the Distribution network of the Power System. The various simulations and analysis of signals is done in the MATLAB® environment.

KEYWORDS:

1.      Artificial neural networks

2.      Feedforward networks

3.      Back propagation algorithm

4.      Levenberg–Marquardt algorithm

SOFTWARE: MATLAB/SIMULINK

 CIRCUIT DIAGRAM:

Figure 1 Snapshot of the studied model.

 EXPERIMENTAL RESULTS:

Figure 2 Data pre processing illustration.

Figure 3 Regression FIT of the outputs vs. targets for the network.

 CONCLUSION:

In this paper we have studied the application of artificial neural networks for the detection and classification of faults on a three phase transmission lines system. The method developed utilizes the three phase voltages and three phase currents as inputs to the neural networks. The inputs were normalized with respect to their pre-fault values respectively. The results shown in the paper is for line to ground fault only. The other types of faults, e.g. line-to-line, double line-to-ground and symmetrical three phase faults can be studied and ANNs can be developed for each of these faults. All the artificial neural networks studied here adopted the back-propagation neural network architecture. The simulation results obtained prove that the satisfactory performance has been achieved by all of the proposed neural networks and are practically  implementable. The importance of choosing the most appropriate ANN configuration, in order to get the best performance from the network, has been stressed upon in this work. The sampling frequency adopted for sampling the voltage and current waveforms in this research work is 1,000 Hz. Some important conclusions that can be drawn from the research are:

1. Artificial neural networks are a reliable and effective method for an electrical power system transmission line fault classification and detection especially in view of the increasing dynamic connectivity of the modern electrical power transmission systems.

2. The performance of an artificial neural network should be analyzed properly and particular neural network structure and learning algorithm before choosing it for a practical application.

3. Back propagation neural networks delivers good performance, when they are trained with large training data set, which is easily available in power systems and hence back propagation networks have been chosen for proposed method.

The scope of ANN is wide enough and can be explored more. The fault detection and classification can be made intelligent by nature by developing suitable intelligent techniques.  This can be achieved if we have the computers which can handle large amount of data and take least amount time for calculations.

REFERENCES:

Alanzi EA, Younis MA, Ariffin AM (2014) Detection of faulted phase type in distribution systems based on one end voltage measurement. Electr Power Energy Syst 54:288–292 Jamil et al. SpringerPlus (2015) 4:334 Page 13 of 13

Aziz MS, Abdel MA, Hassan M, Zahab EA (2012) High-impedance faults analysis in distribution networks using an adaptive neuro fuzzy inference system. Electr Power Compon Syst 40(11):1300–1318

Bouthiba T (2004) Fault location in EHV transmission lines using artificial neural networks. Int J Appl Math Comput Sci 14(1):69–78

Chaturvedi DK (2008) Soft computing techniques and its applications in electrical engineering. Springer, Berlin, Heidelberg

Chaturvedi DK, Mohan M, Kalra PK (2004) Improved generalized neuron model for short-term load forecasting. Soft Comput 8:370–379

Optimization techniques to enhance the performance of induction motor drives: A review

ABSTRACT:  

Induction motor (IM) drives, specifically the three-phase IMs, are a nonlinear system that are difficult to explain theoretically because of their sudden changes in load or speed conditions. Thus, an advanced controller is needed to enhance IM performance. Among numerous control techniques, fuzzy logic controller (FLC) has increasing popularity in designing complex IM control system due to their simplicity and adaptability. However, the performance of FLCs depends on rules and membership functions (MFs), which are determined by a trial and- error procedure. The main objective of this paper is to present a critical review on the control and optimization techniques for solving the problems and enhancing the performance of IM drives. A detailed study on the control of variable speed drive, such as scalar and vector, is investigated. The scalar control functions of speed and V/f control are explained in an open- and closed-loop IM drive. The operation, advantages, and limitations of the direct and indirect field-oriented controls of vector control are also demonstrated in controlling the IM drive. A comprehensive review of the different types of optimization techniques for IM drive applications is highlighted. The rigorous review indicates that existing optimization algorithms in conventional controller and FLC can be used for IM drive. However, some problems still exist in achieving the best MF and suitable parameters for IM drive control. The objective of this review also highlights several factors, challenges, and problems of the conventional controller and FLC of the IM drive. Accordingly, the review provides some suggestions on the optimized control for the research and development of future IM drives. All the highlighted insights and recommendations of this review will hopefully lead to increasing efforts toward the development of advanced IM drive controllers for future applications.

KEYWORDS:

1.      Induction motor drive

2.      Optimization algorithms

3.      Scalar control

4.      Vector control

5.      Fuzzy logic controller

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

Fig. 1. Architecture of the IM control system.

 CLOSED-LOOP OF SCALAR CONTROL FOR IM DRIVE:

Fig. 2. Closed-loop of scalar control for IM drive.

BLOCK DIAGRAM OF DFOC FOR IM DRIVE

Fig. 3. Block diagram of DFOC for IM drive.

BLOCK DIAGRAM OF IFOC FOR IM DRIVE

Fig. 4. Block diagram of IFOC for IM drive.

 BLOCK DIAGRAM OF DTC FOR IM DRIVE

Fig.5 Block diagram of DTC for IM drive

OPTIMIZATION TECHNIQUE BASED ON PID SPEED CONTROLLER FOR SCALAR CONTROL

Fig. 6. Optimization technique based on PID speed controller for scalar control

OPTIMIZATION TECHNIQUE BASED ON PID CONTROLLERS FOR (A) DFOC AND (B) IFOC

(a)

(b)

Fig. 7. Optimization technique based on PID controllers for (a) DFOC and (b) IFOC.

OPTIMIZATION TECHNIQUE BASED ON FUZZY LOGIC SPEED CONTROLLER FOR SCALAR CONTROL.

Fig. 8. Optimization technique based on fuzzy logic speed controller for scalar control.

OPTIMIZATION TECHNIQUE BASED ON FLC CONTROLLERS FOR (A) DFOC AND (B) IFOC.

(a)

(b)

Fig. 9. Optimization technique based on FLC controllers for (a) DFOC and (b) IFOC.

CONCLUSION:

In this paper, an Indirect Field-Oriented Control (IFOC) scheme for a drive system of three-phase induction motor is effectively investigated and validated using various simulation results in Matlab/Simulink. The performance of proposed controller is verified by introducing variation in speed and load torque. Simulation results demonstrate that PI has sluggish response compared to AFLC. In all load torque variations, the proposed AFLC shows robustness and continues to track the reference with small steady-state error. Moreover, AFLC based on LM is robust to model parameter variations, load variations and less sensitive to uncertainties and disturbances. The proposed scheme verifies superior and smoother performance with improved dynamic response.  Furthermore, the effectiveness of proposed AFLC is evaluated and justified from performance indices IAE, ISE and ITAE.

REFERENCES:

1. Leonhard W (1996) Controlled AC drives, a successful transfer from ideas to industrial practice. Control Eng Pract 4(7):897–908

2. Fitzgerald AE,KingsleyCU, StephenD(1990) Electricmachinery, 5th edn. McGraw-Hill, New York

3. Marino R, Peresada S, Valigi P (1993) Adaptive input-output linearizing  control of induction motors. IEEE Trans Autom Cont 38(2):208–221

4. Leonhard W (1985) Control of electrical drives. Springer-, Berlin 

5. HeinemannG(1989) Comparison of several control schemes for ac induction motors. In: Proceedings of European Power Electronics Conference (EPE’89), pp 843–844

Indirect field-oriented control of induction motor drive based on adaptive fuzzy logic controller

ABSTRACT:  

Recently, Asynchronous Motors are extensively used as workhorse in a multitude of industrial and high performance applications. Induction Motors (IM) have wide applications in today’s industry because of their robustness and low maintenance. A smart and fast speed control system, however, is in most cases a prerequisite for most applications. This work presents a smart control system for IM using an Adaptive Fuzzy Logic Controller (AFLC) based on the Levenberg–Marquardt algorithm. A synchronously rotating reference frame is used to model IM. To achieve maximum efficiency and torque of the IM, speed control was found to be one of the most challenging issues. Indirect Field-Oriented Control (IFOC) or Indirect Vector Control techniques with robust AFLC offer remarkable speed control with high dynamic response. Computer simulation results using MATLAB/Simulink® Toolbox are described and examined in this study for conventional PI and AFLC. AFLC presents robustness as regards overshoot, undershoot, rise time, fall time, and transient oscillation for speed  variation of IFOC IM drive in comparison with classical PI. Moreover, load disturbance rejection capability for the designed control scheme is also verified with the AFL controller.

KEYWORDS:

1.      Induction Motor (IM)

2.      Indirect Field-Oriented Control (IFOC)

3.       Pulse Width Modulation (PWM)

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

Fig. 1 Proposed system block diagram using AFLC

EXPERIMENTAL RESULTS:

Fig. 2 dq axis stator currents  for both AFLC & PI controller

Fig. 3 Stator phase voltage for both AFLC & PI controller

Fig. 4 Stator phase current for both AFLC & PI controller

Fig. 5 Rotor speed under variable load torque, a comparison of AFLC based on LM & PI

Fig. 6 dq-axis stator currents for both AFLC & PI controller

Fig. 7 Stator phase voltage for both AFLC & PI controller

Fig. 8 Stator phase current for both AFLC & PI controller

Fig. 9 Rotor speed under variable load torque, a comparison of AFLC based on LM & PI

Fig. 10 dq-axis stator currents for both AFLC & PI controller

Fig. 11 Stator phase voltage for both AFLC & PI controller

Fig. 12 Stator phase current for both AFLC & PI controller

 CONCLUSION:

In this paper, an Indirect Field-Oriented Control (IFOC) scheme for a drive system of three-phase induction motor is effectively investigated and validated using various simulation results in Matlab/Simulink. The performance of proposed controller is verified by introducing variation in speed and load torque. Simulation results demonstrate that PI has sluggish response compared to AFLC. In all load torque variations, the proposed AFLC shows robustness and continues to track the reference with small steady-state error. Moreover, AFLC based on LM is robust to model parameter variations, load variations and less sensitive to uncertainties and disturbances. The proposed scheme verifies superior and smoother performance with improved dynamic response.  Furthermore, the effectiveness of proposed AFLC is evaluated  and justified from performance indices IAE, ISE and ITAE.

REFERENCES:

1. Leonhard W (1996) Controlled AC drives, a successful transfer from ideas to industrial practice. Control Eng Pract 4(7):897–908

2. Fitzgerald AE,KingsleyCU, StephenD(1990) Electricmachinery, 5th edn. McGraw-Hill, New York

3. Marino R, Peresada S, Valigi P (1993) Adaptive input-output linearizing  control of induction motors. IEEE Trans Autom Cont 38(2):208–221

4. Leonhard W (1985) Control of electrical drives. Springer-, Berlin 

5. HeinemannG(1989) Comparison of several control schemes for ac induction motors. In: Proceedings of European Power Electronics Conference (EPE’89), pp 843–844