International Journal of Power and Energy Conversion (14 papers in press)
Improved MPPT Control for Single-phase Grid-Connected PV System
by Mahmoud Salem, Yousry Atia, Osama Arafa
Abstract: In single-phase PV grid-connected systems, the dc-link voltage is characterized by a ripple whose frequency is double the grid-frequency. The voltage ripples always prevent MPPT controllers from capturing the maximum available PV power and it also affects the efficiency of the whole system. This paper introduces the analysis and design of an improved MPPT control strategy for single-phase two-stage PV grid-connected system (PVGCS). The improved MPPT control strategy is used to eliminate the side effects of the voltage ripple on the extracted PV maximum power. This paper shows how the double grid-frequency voltage ripples affect the extracted PV power and the MPPT controller performance. This analysis indicates by equations that the extracted power from the PV has an average value which is less than the maximum value of the available PV power and finds the relation between this value and the double grid frequency voltage ripples. The paper also proposes a modified MPPT strategy to overcome this phenomenon. The modified strategy is tested with two known MPPT controllers, the incremental conductance and the ripple correlation controller. Superior performance regarding MPPT performance and ripple voltage mitigation is validated in both cases through Simulink simulations.
Keywords: : Incremental conductance (IC); ripple correlation control (RCC); maximum power point tracking (MPPT); single-phase two-stage grid-connected PV.
Identification of Nonlinear Dynamic System using Machine Learning Techniques
by Ranjeeta Bisoi
Abstract: Identification of nonlinear systems finds extensive applications in control design and stability analysis. To identify complex nonlinear systems, the neural network has drawn the attention of many researchers due to its broad application area. In this paper, an improved identification method based on Robust Regularized Exponentially Extended Random Vector Functional Link Network (RERVFLN) has been proposed for nonlinear system identification. The input is extended using trigonometric expansion which increases the accuracy of the algorithm. To verify the accuracy of the proposed model, some benchmark Monte Carlo simulations are carried out through simulation study and the obtained results are compared with some established techniques such as original RVFLN, ELM, and LMS. Prediction accuracy of the proposed method RERVFLN is higher than the normal RVFLN for different nonlinear systems which is clear from the performance evaluation section.
Keywords: Robust Regularized Exponentially Extended RVFLN; identification; nonlinear systems; Monte Carlo simulations; Extreme Learning Machine; Least Mean Square algorithm.
Wind Speed and Power prediction using MK-PINN
by S.P. Mishra, R.N. Senapati
Abstract: A Multi kernel Pseudo Inverse neural network (MKPINN) is proposed in this paper for efficient wind speed and power forecasting. The proposed model has been compared with Gaussian, wavelet, Polynomial and Sigmoid kernel. To get best output and learning methodology and stability Pseudo Inverse neural network is added, which substitutes the hidden layer with kernel function. This helps to achieve more accurate and faster response. Various case studies have been carried out from 10 mins to 5 hours interval in order to prove it accuracy
Keywords: Pseudo Inverse neural network; Multi-kernel learning algorithm (MKL).
4E analysis and multi objective optimization for a solar hybrid steam power plant Using ABC algorithm: A case study in Iran
by Davood Beyralvand, Mahmood Yaghoubi
Abstract: The objectives of this study are 4E analysis and multi objective optimization for solar hybrid fossil fuel power generation system and steam power plant without solar field (W.S.F) of Karaj during a year. The analysis is performed using MATLAB programing, and validated with a power plant unit data. The results show that system with oil-water heat exchanger (solar hybrid system), has less CO2 emission and fuel consumption than W.S.F power plant but 2.8% more exergy efficiency. In this system, the Levelized cost of electricity is increased by 15.2%. The annual average contribution of steam on the production of electricity during sunny hours and during the day, are 15% and 7.3%, respectively. By using Artificial Bee Colony (ABC) algorithm, system is optimized for maximum exergy efficiency and minimum cost of produced electricity. By selecting the final optimum solution, the fuel cost and CO2 emission are reduced by 9.96% from their corresponding values from base case. Optimization process improves the total performance of the power plant in a way that total exergy destruction is reduced 6.18% and average energy and exergy efficiency are increased from 37.45% to 42.43% and 35.58% to 40.74% respectively. Also, the results show that multi objective optimization work correctly, net power of the plant is increased about 15.81%. Furthermore, sensitivity analysis is performed to study the solar tracking modes, changing the number of collector loops, capacity of the power plant, interest rate and plant economic life on unit cost.
Keywords: Solar hybrid steam power plant; fossil fuel power generation system; CO2 emission; TRR; ABC algorithm; 4E analysis.
Design of fractional order proportional integral controller for load frequency control of multi area power system under deregulated environment
by Kurakula Vimala Kumar, V. Ganesh
Abstract: The main objective of presented article is here to focus how efficiently minimise the deviations in frequency and area control error caused by load fluctuations and uncertainties in load under the deregulated power system. This work is carried out to eliminate the frequency errors by using fractional order proportional integral (FOPI) controller under deregulated environment by considering the effect of one possible bilateral contract scenario. Because of system nonlinearities, uncertainties and continuously fluctuant load demand the design of these controllers is quite complicated in deregulated environment. The proposed work is to enhance the system parameters like transmitted line power, frequency deviation error, and area control error (ACE) using fractional order PI controller for hydro-thermal system and thermal-thermal system under deregulated environment. The results have been analysed with classical integer order PI controller and FOPI controller. It is observed that the efficacy of the results is satisfied and improved when compared with previous work.
Keywords: LFC; AGC; PI controller; FOPI controller; integer order PI controller.
Finite element validation of the analytical model of variable reluctance motor
by Souhir Tounsi
Abstract: In this paper, we present a methodology for the parameterised modelling of a synchronous motor with variable reluctance. This methodology is based on the analytical method offering the possibility of the coupling of the analytical programme to optimisation algorithms of large dimensions since this method is fast in term simulation time. The analytical model is validated by the finite element method in two dimensions in linear regime and in saturated regime.
Keywords: variable reluctance motor; analytic method; finite element method; validation.
Solar power forecasting using robust kernel extreme learning machine and decomposition methods
by Irani Majumder, Ranjeeta Bisoi, Niranjan Nayak, Naeem Hannoon
Abstract: This paper proposes empirical mode decomposition (EMD)-based robust kernel extreme learning machine (RKELM) to achieve a precise predicted value of solar power generation in a smart grid environment. The non-stationary historical solar power data is initially decomposed into various intrinsic mode functions (IMFs) using EMD, which are subsequently passed through the proposed robust Morlet wavelet kernel extreme learning machine (RWKELM) for solar power prediction at different time horizons. Further a reduced kernel matrix version of RWKELM is used to decrease the training time significantly without appreciable loss of forecasting accuracy. By implementing the real time data for validation of the proposed method for short term solar power prediction it can be observed that the proposed EMD-based RWKELM outperforms various other methods, in terms of different performance matrices and execution time. The solar power prediction results on experimental data show the lowest error which proves the highest prediction accuracy.
Keywords: solar power forecasting; empirical mode decomposition; EMD; wavelet transform decomposition; WD; extreme learning machine; ELM; robust kernel extreme learning machine; RKELM; reduced kernel matrix.
Modelling and control design of a multi-source renewable energy system with coupled DC/DC converters power compensation
by Sihem Djebbri, Abderrezak Metatla, Samir Ladaci
Abstract: This paper investigates the electrical energy transfers control in a multi-source renewable energy system. The considered system is composed of two sources (photovoltaic and lead-acid battery bank), a specific zero voltage switch full-bridge isolated buck DC/DC power converter and the load. The main contribution of this paper is to propose a modelling approach that details the coupling and uncoupling of DC/DC power converter on a DC bus of multisource renewable energy systems. The proposed model is suitable for control and performance evaluation. This modelisation, results in a system with two cascade loops, the inner loop which allows the control of the current and the outer loop which controls the output voltage using a PI and a PID two controllers respectively. We show by means of numerical simulation tests that the proposed control gives good results in terms of robustness and stability despite the fluctuations of renewable sources and the load variations.
Keywords: multi-source renewable energy system; modelling; PID control; DC bus; performance; robustness.
Design and simulation of a low-power low-cost high-frequency transformer-based dynamic voltage restorer for household applications
by Mohammad Farhadi-Kangarlu
Abstract: Sudden variations of voltage can be damaging for voltage-sensitive loads. Dynamic voltage restorer (DVR), which is a power-electronic converter-based device, is an economical solution for voltage variations compensation. In this paper, a high-frequency (HF) transformer-based DVR is designed for household applications. As the household application is considered, the DVR will be low-power and it should be designed to be compact and economic. Therefore, the HF transformer is used in the design in order the system to be more compact and economic. Also, unlike other HF transformer-based DVR structures, no energy storage is used and the number of active converter stages is minimised. The topology selection and compensation capacity are justified in the paper. The simulation results of the designed DVR are presented to verify its performance.
Keywords: dynamic voltage restorer; DVR; high-frequency transformer; power quality; voltage sag; voltage compensation.
Implementation of PWM technique with soft starting algorithm for three-phase induction motor drives
by Rajan Vinodray Vamja, Rakesh Maurya, Shaikh Mohammed Suhel
Abstract: In this paper, a study on issues related to starting of induction motor is carried out. Performance comparisons of various starting methods are also studied. To investigate the performance of ramp soft starting algorithm, sinusoidal pulse width modulation (SPWM) and space vector pulse width modulation (SVPWM) techniques are implemented with voltage source inverter (VSI) fed induction motor. Detailed FFT analysis of stator voltage and current of induction motor is studied under wide range of speed variations. Based on experimental study, few recommendations for implementation of boost start-up and s-curve start-up algorithm in Microcontroller (STM32F407VG) are made for high inertial load. Experimental results are obtained and its performance analysis in view of FFT of motor current is also carried out for aforesaid PWM techniques.
Keywords: soft starting; adjustable speed drive; ASD; sinusoidal pulse width modulation; SPWM; space vector pulse width modulation; SVPWM; control voltage source inverter; boost start-up; torque; synchronous PWM; asynchronous PWM.
An intelligent scheme for categorising fault events in compensated power network using K-nearest neighbour technique
by Sunil Kumar Singh, D.N. Vishwakarma, R.K. Saket
Abstract: This paper describes a machine learning-based intelligent scheme for categorisation of fault events in series compensated power network using K-nearest neighbour technique. The wavelet decomposition mechanism is applied on 3-phase post fault current signal for digging out the significant features of the shunt fault events in this proposed scheme. Afterward, K-nearest neighbour-based classifier model has been utilised for ascertaining the specific category of the fault events in the network. The category of the shunt events in the network has been ascertained on the basis of the estimated entropy of the wavelet coefficients. The feasibility and strength of proposed scheme for categorising the fault events in the compensated power network are assessed for different fault events with varying network situations in simulated compensated power network. Finally, it is observed that the proposed discrete wavelet transform and K-nearest neighbour-based scheme is very effectual in identifying the specific fault events in the compensated power network and is unaffected by changing system circumstances.
Keywords: discrete wavelet transform; machine learning; K-nearest neighbour; K-NN; fault diagnosis; protection of compensated power network.
Sensorless control based on the improved VM NN SC MRAS method for high performance SPIM drives using LPF
by Ngoc Thuy Pham, Thuan Duc Le, Vu Thanh Tran, Nho Van Nguyen
Abstract: This paper proposes a novel Stator Current Model Reference Adaptive System based scheme using neural network (NNSM_SC_MRAS) for sensorless controlled of Six-Phase Induction Motor (SPIM) drives. For this scheme, the measured stator current components are used as the reference model and a two layer linear NN stator current observer is used as an adaptive model. The voltage model (VM) rotor flux identifier with value of stator resistor is update online is used to provide the rotor flux for the adaptive model, this helps to overcome the instability problem and enhance the performance of the observer. Especially, In order to eliminate the drift problems, the pure integrator of VM is replaced with a first-order low-pass filter, and the error due to this replacement is also compensated in proposed scheme. Simulation results have demonstrated that the performance of the proposed observer is significantly improved especially at low and near zero speed range.
Keywords: MRAS observer; neural network; sensorless vector control; six-phase induction motor drive.
Optimal sizing and placement of the UPQC and DG simultaneously based on sensitivity analysis and firefly algorithm
by S. Karimi, K. Gholami, M. Rizwan
Abstract: The efficient operation of power system is utmost important for reliable power supply to customers. Compensators such as the distributed generation (DG) and power electronics-based devices play the major roles in this regard. In this paper, two kinds of compensators, DG and unified power quality conditioner (UPQC), are allocated to minimize the real power loss and improve voltage indices. In order to achieve this purpose, the loss sensitivity factor (LSF) is utilized to find the specific nodes for the DG and UPQC and then the firefly algorithm (FA) is used to find the sizing and location of DG and UPQC among the estimated nodes. The proposed work is accomplished on IEEE 34 and 69-bus systems. Obtained results are compared with other existing approaches and found better. It is reveal that the proposed approach is effective for the optimal sizing and placement of custom devices with DG simultaneously.
Keywords: distributed generation; unified power quality conditioner; sensitivity analysis; firefly algorithm.
Optimal design of battery charge management controller for hybrid system PV/wind cell with storage battery
by Mohamed M. Ismail, Ahmed F. Bendary, Mahmoud Elsisi
Abstract: The renewable energy sources (RES) became a fundamental necessity to the majority of electric systems and also the isolated systems like the water pumping and petroleum excavators systems. The RES typically required energy storage system because of variations in the power during the day. Because of increment popular of utilising batteries, the charging and discharging procedure of battery system should have been well managed through an adaptive controlled system. In this paper, a standalone power system utilising photovoltaic, wind energy and batteries are contributed in providing the required load. The charging balance of batteries is accomplished by utilising a new hybrid genetic algorithm (GA)-based proportional integral (PI) controller with adaptive neuro-fuzzy inference system (ANFIS). This new hybrid GA-based PI with ANFIS control method is compared with the GA-based PI controller and the conventional PI controller based on Ziegler Nichols (ZN) technique. Dynamic simulations exhibit the right performance of the hybrid GA-based PI with ANFIS control for the proposed power system under study and the better performance than the GA-based PI controller and ZN-based PI controller.
Keywords: photovoltaic; wind generation; batteries; ANFIS; genetic algorithm; GA.