Showing papers in "The Journal of Engineering in 2017"

Techno-economic analysis of a PV–wind–battery–diesel standalone power system in a remote area

Abstract: The global acceptance of solar and wind resources for power generation has continued to increase due to the fluctuation of world oil and gas prices, recent advances in technology, high prices of fossil fuels, no direct greenhouse gas emission from solar and wind resources and government policies to support utilisation of renewable energy resources (RESs). In recent times, RESs have become a potential alternative to supply electricity to the rural communities where the extension of transmission and distribution lines is difficult due to technical and financial barriers. In view of this, this study deals with the optimisation of net present cost (NPC), fuel cost, operation cost and cost of energy (COE) of the hybrid system which consists of photovoltaic (PV), wind turbine generator (WTG), diesel generator and battery storage system. A hybrid power system is designed in this research work to serve a remote community. The optimisation of the key performance indicators of the proposed hybrid power system is done by using the hybrid optimisation model for electric renewable (HOMER). The results obtained from this research work are analysed to select the best options among the available configurations based on the lowest NPC and COE produced by each configuration. The simulation results from several case studies show that incorporation of PV and WTG have reduced the operating cost of the system.

Representational learning approach for power system transient stability assessment based on convolutional neural network

Abstract: The transient stability assessment (TSA) problem can be mapped into a two-class classification problem in machine learning, which estimates the dynamic security boundary of the power system by learning from large amount samples. A representational learning approach is proposed to solve the problem based on big data collected from Phasor Measurement Units (PMUs), which includes four stages: (i) Construct original input features by using PMUs data to describe the dynamic characteristics of the power system. (ii) Unsupervised representational feature learning by using the original features. Stacked autoencoders (SAEs) perform representational learning for crucial features. (iii) Supervised classifier training. A powerful deep learning model, convolutional neural network, which is added to SAE, is trained and tested with the learned representation. (iv) Online application, the trained model is applied to the online evaluation for TSA. Simulation on the New England 39-bus test system shows that the proposed approach has high accuracy, rare misclassification of the unstable sample and excellent robustness with noise in PMUs for TSA.

Optimal Power Flow Techniques under Characterization of Conventional and Renewable Energy Sources: A Comprehensive Analysis

Abstract: The exhaustive knowledge of optimal power flow (OPF) methods is critical for proper system operation and planning, since OPF methods are utilized for finding the optimal state of any system under system constraint conditions, such as loss minimization, reactive power limits, thermal limits of transmission lines, and reactive power optimization. Incorporating renewable energy sources optimized the power flow of system under different constraints. This work presents a comprehensive study of optimal power flows methods with conventional and renewable energy constraints. Additionally, this work presents a progress of optimal power flow solution from its beginning to its present form. Authors classify the optimal power flow methods under different constraints condition of conventional and renewable energy sources. The current and future applications of optimal power flow programs in smart system planning, operations, sensitivity calculation, and control are presented. This study will help the engineers and researchers to optimize power flow with conventional and renewable energy sources.

High-performance full adder architecture in quantum-dot cellular automata

Abstract: Quantum-dot cellular automata (QCA) is a new and promising computation paradigm, which can be a viable replacement for the complementary metal–oxide–semiconductor technology at nano-scale level. This technology provides a possible solution for improving the computation in various computational applications. Two QCA full adder architectures are presented and evaluated: a new and efficient 1-bit QCA full adder architecture and a 4-bit QCA ripple carry adder (RCA) architecture. The proposed architectures are simulated using QCADesigner tool version 2.0.1. These architectures are implemented with the coplanar crossover approach. The simulation results show that the proposed 1-bit QCA full adder and 4-bit QCA RCA architectures utilise 33 and 175 QCA cells, respectively. Our simulation results show that the proposed architectures outperform most results so far in the literature.

Membrane Fouling and Chemical Cleaning in Three Full-Scale Reverse Osmosis Plants Producing Demineralized Water

Abstract: Membrane fouling and cleaning were studied in three reverse osmosis (RO) plants. Feed water was secondary wastewater effluent, river water, and surface water. Membrane autopsies were used for fouling characterization. Fouling layer measurements included total organic carbon (TOC), adenosine triphosphate, polysaccharides, proteins, and heterotrophic plate counts. In all locations, membrane and spacer fouling was (bio)organic. Plant chemical cleaning efficiencies were evaluated from full-scale operational data and cleaning trials in a laboratory setup. Standard cleaning procedures were compared to two cleaning procedures specifically adapted to treat (bio)organic fouling using commercial blend cleaners (mixtures of active substances). The three RO plants were impacted by irreversible foulants causing permanently decreased performance in normalized pressure drop and water permeability even after thorough chemical cleaning. The standard plant and adapted cleaning procedures reduced the TOC by 45% on average, with a maximum of ~80%. In general, around 20% higher biomass removal could be achieved with adapted procedure I compared to adapted procedure II. TOC measurements and SEM showed that none of cleaning procedures applied could remove foulants completely from the membrane elements. This study underlines the need for novel cleaning approaches targeting resistant foulants, as none of the procedures applied resulted in highly effective membrane regeneration.

Global MPPT algorithm with coordinated control of PSO and INC for rooftop PV array

Abstract: Considering the characteristics of rooftop photovoltaic (PV) array, a new mathematical model of PV array that is suitable for partial shading condition is built based on an engineering model of a solar cell. It is derived theoretically that output voltage of PV array can be expressed by piecewise function. Moreover, a global maximum power point tracking (MPPT) control method based on particle swarm optimisation (PSO) and incremental conductance algorithm (INC) coordination control is put forward under partial shading. When the environmental conditions are stable, MPPT can be achieved by INC control; otherwise, when the external conditions change, global maximum power point of PV systems can be located fast and accurately by PSO. So, the tracking efficiency is improved. Simulation results show that the PSO and INC coordination control methods have better tracking precision. It can be achieved easily and reduce hardware input costs. It also improves the PV array output efficiency.

Improved BP neural network algorithm to wind power forecast

Abstract: To constantly enhance the accuracy of wind power prediction and furthermore reduce the uncertainty of power grid dispatching, this study proposes an improved back propagation (BP) neural network algorithm. The original prediction method of BP neural network algorithm has been improved, and the traditional minimum square error (SE) perform function is abandoned. Maximum correntropy criteria (MCC) algorithm which is more conducive to deal with non-Gaussian error and big noise is introduced, and a new perform function is created. Through the analysis of examples, the feasibility of MCC algorithm is verified. Comparing to the traditional mean SE (MSE) perform function, MCC perform function could drop the limit error of prediction, reduce root MSE and increase the correlation between forecasting power and real power. The most important is that the prediction accuracy is enhanced.

Planning of electric vehicle charging station based on queuing theory

Abstract: EV charging station is a very important link in urban planning, and the design of the position and capacity of charging stations are not only to meet the public demand for traveling, but also to maintain the stability of operation and management in power grid. Considering the non-simultaneity and randomness of the EVs arriving at the charging station, the capacity of EV charging stations is calculated by using queuing theory in this study. This study proposes a model which has considered the EVs' charging requirement, economy, and power grid safety to solve the problem of planning EV charging station. Firstly, determine the location of charging station by traffic satisfaction. Secondly, the EV charging stations' charging capacity is optimized with the minimum cost as the optimization target, with the EV user' waiting time patience limit and the distribution network running safety as the constraint condition. Finally, the service area of charging station is divided by the Voronoi diagram. In this study, a 25_nodes traffic network with 24_nodes distribution network underground is modelled in the MATLAB environment to demonstrate the effectiveness of the proposed method. In addition the authors solve the model by the genetic algorithm in MATLAB.

Whale optimisation algorithm for photovoltaic model identification

Abstract: Parameter extraction of a solar cell is essential in the simulation and design calculation of photovoltaic (PV) systems. The mathematical model of the PV module is a non-linear I – V characteristic including several unknown parameters as the PV manufacturers’ data are not sufficient. This study presents a method for estimating the parameters of the single- and double-diode PV models of a PV module based on a nature-inspired meta-heuristic optimisation algorithm known as the whale optimisation algorithm (WOA). The validity of the proposed WOA-based PV model is verified by comparing its simulation results with the experimental results for the PV modules under different environmental conditions.

Suppression of synchronous resonance for VSGs

Abstract: The virtual synchronous generator (VSG) is an attractive interfacing technique for high-penetration renewable generation. By incorporating the inertia control, the grid-connected voltage-source converter can behave in a similar way with the SGs, which is helpful to enhance the stability of the power system. However, it is reported that the synchronous frequency resonance (SFR) can be aroused in the VSG due to the resonance peaks in the power control loops at the fundamental frequency. By modelling the power control loop in the dq domain, the mechanism underlying the SFR is studied. It reveals the frequency shift of the grid impedance in dq frame is the origin of SFR. Moreover, the phase-amplitude cross-regulation scheme is proposed to suppress the SFR. In this way, the resonance peaks in the power control loops are completely removed, and the coupling effects between the active power and reactive power loops are also eliminated. Therefore, superior power control performances can be achieved. Simulation results verify the effectiveness of the theoretical analysis and both dynamic performance and stability of the power loops are greatly improved by the proposed cross-regulation scheme.

Enhancing short-term probabilistic residential load forecasting with quantile long–short-term memory

Abstract: In the study of load forecasting, short-term (ST) load forecasting in the horizon of individuals is prone to manifest non-stationary and stochastic features compared with predicting the aggregated loads. Hence, better methodologies should be proposed to forecast ST residential loads more accurately, and refined representation of forecasting results should be reconsidered to make the prediction more reliable. A format of ST probabilistic forecasting results in terms of quantiles is offered, which can better describe the uncertainty of residential loads, and a deep-learning-based method, quantile long–ST memory, to implement probabilistic residential load forecasting. Experiments are conducted on an open dataset. Results show that the proposed method overrides traditional methods significantly in terms of average quantile score.

Low voltage ride-through capability enhancement of grid-connected permanent magnet synchronous generator driven directly by variable speed wind turbine: a review

Abstract: The large power penetration of wind farms into the power grids requires new regulations by transmission system operators to keep them connected to the grid as long as possible. Grid disturbances such as voltage dips cause islanding of wind farms on the way to protect its apparatus from damage due to a high current flowing. The grid stability will suffer due to the islanding of large-scale wind farms. Wind farms should keep on connecting to the grid during low voltages for a specific time [low voltage ride through (LVRT) capability] to support the grid stability restoring. LVRT capability of permanent magnet synchronous generator driven directly by a variable speed wind turbine (PMSG-VSWT) can be realised by modifying the control of grid side converter (GSC), machine side converter (MSC), pitch angle control, or using the existing energy storage system. This study presents a review of recent proposed improvements to enhance the LVRT capability of PMSG-VSWT. Many artificial intelligence and conventional controllers are used to enhance the control performance during voltage sags to keep the wind farms connected to the grid according to the recent grid codes.

Energy management system controller for a rural microgrid

Abstract: An energy management system (EMS) for an isolated microgrid in a typical rural location is presented. The rural microgrid consists of locally available renewable energy resources, such as solar, wind, along with diesel generator for backup and battery as storage to meet the electrical load demand. The grid electricity supply in the village is characterised by frequent outages with the poor quality of supply. Firstly, the proposed system controller's (PSC) dispatch rules are formulated using MATLAB. Then, PSC rules designed in MATLAB are integrated with hybrid optimisation of multiple electric renewables (HOMER PRO) software to design an isolated microgrid. A detailed techno-economic analysis is presented to obtain the cost-effective sizing of the microgrid considering a yearly load growth over the project lifetime. Finally, to show the effectiveness, a comparative analysis of the PSC with the inbuilt load following controller of the HOMER PRO is also illustrated. Also, a simple procedure showing the integration of MATLAB with HOMER PRO is outlined briefly.

Protection of micro-grid systems: a comprehensive survey

Abstract: The emergence of renewable energy with high consumers' energy demand has led to the concept of micro-grids in the world in recent years. The development and expansion of micro-girds with renewable energy is facing challenges with the protection needing serious attention. The challenges result from the difficulties in coordinating protection devices. The presence of various energy sources makes the micro-grid protection sensitive to changes in current and voltage levels. A micro-grid operates in parallel or independently with the existing utility network as grid-connected mode or islanded mode, respectively. Whatever the mode of operation, the protection system has to detect short circuits and clear faults to isolate only the faulty portions. In this study, micro-grid protection comprehensive review is performed to discuss the present situation. Protection issues and existing protection schemes for micro-grid are presented based on the previous achievement in the area. This work will contribute in the assessment and implementation of micro-grid protection to provide reliable and safe electricity to consumers.

Comparison of EV smart charging strategies from multiple stakeholders' perception

Abstract: Electric vehicles (EVs) will become an integral part of the future smart grid. Random charging of EVs may give birth to many issues such as increasing losses, voltage deviation, and increase in peak. The threats imposed by random charging can be conquered by smart or coordinated charging strategies. The liberalisation of energy sector creates an opportunity for different market actors to use flexible EV demand for their own benefits. Thus, the objectives for smart EV charging can be formulated to meet the interest of a single stakeholder or multiple stakeholders. In this study, a comparative analysis of three smart charging strategies from different stakeholders' perception/interests – (i) aggregator (also representing customers), (ii) Network operator, and (iii) both aggregator and network operator simultaneously – while considering different EV penetration is presented in terms of increase in peak load, peak-valley difference, load factor, and total charging cost. The influence of fast charging and battery charging efficiency on these results is also discussed.

Experimental Study on Strain Reliability of Embroidered Passive UHF RFID Textile Tag Antennas and Interconnections

Abstract: We present embroidered antennas and interconnections in passive UHF RFID textile tags and test their strain reliability. Firstly, we fabricate tag antennas on two different stretchable fabric substrates by five different embroidery patterns and choose the most stretchable ones for testing. Next, the tag ICs are attached by sewing and gluing, and the tag reliability during repeated stretching cycles is evaluated through wireless measurements. Initially, the chosen tags achieve read ranges of 6–8 meters and can strain up to 140–150% of their original length. After 100 stretching cycles to 80% of their maximum strain, the read ranges of the tags with glued interconnections are similar to the initial values. In addition, also the read ranges of the tags with sewed interconnections are still more than 70%–85% of their initial values. However, some challenges with the reproducibility need to be solved next.

A Condensed Introduction to the Doubly Fed Induction Generator Wind Energy Conversion Systems

Abstract: The increase in wind power penetration, at 456 GW as of June 2016, has resulted in more stringent grid codes which specify that the wind energy conversion systems (WECS) must remain connected to the system during and after a grid fault and, furthermore, must offer grid support by providing reactive currents. The doubly fed induction generator (DFIG) WECS is a well-proven technology, having been in use in wind power generation for many years and having a large world market share due to its many merits. Newer technologies such as the direct drive gearless permanent magnet synchronous generator have come up to challenge its market share, but the large number of installed machines ensures that it remains of interest in the wind industry. This paper presents a concise introduction of the DFIG WECS covering its construction, operation, merits, demerits, modelling, control types, levels and strategies, faults and their proposed solutions, and, finally, simulation. Qualities for the optimal control strategy are then proposed. The paper is intended to cover major issues related to the DFIG WECS that are a must for an overview of the system and hence serve as an introduction especially for new entrants into this area of study.

Gird-connected boost inverter for low-power PV applications with model predictive control

Abstract: We present a two-stage inverter with high-voltage conversion ratio employing modified finite-set model predictive control (MPC) for utility-integrated low-power photovoltaic (PV) applications. The proposed system is suitable for low PV power applications, the calculated efficiency at 150 W input power and 21 times boosting ratio was around 88%. The presented system is a high gain, transformerless, and with less leakage current system. Switched inductor quadratic boost converter composes the first stage of the proposed system, due to its high step-up ability. It can boost the input voltage up to 30 times. A five-switch current-source inverter represents the dc/ac stage. The MPC algorithm are designed to control the system and perform the following tasks, take out the maximum power (MP) from the PV, controls the proposed system to operate at maximum power, step-up the PV voltage, and introduces low current with low total harmonic distortion (THD) and with unity power factor with the grid voltage. Switching states are selected to reduce the switching losses, but forcing the h-bridge switches to operate at the fundamental frequency.

Fault diagnosis of PV array based on optimised BP neural network by improved adaptive genetic algorithm

Abstract: The fault diagnosis model based on back propagation (BP) neural network is especially suitable for multi-fault and complex pattern recognition. However, the selection of initial weights and thresholds of BP neural network is lack of basis and it is easy to fall into the local optimum. Genetic algorithm (GA) can be used to optimise the initial weights and thresholds of the BP neural network. However, the selection process of GA based on roulette wheel selection is a random operation and the parameters of GA, the crossover probability and the mutation probability, are given a constant value which reduces the efficiency. Thus improved adaptive GA provides improvement in the selection, crossing and mutation process of GA, which prevents the neural network from falling into local optimum more effectively. Results of cases study show that this method can determine the fault types of photovoltaic array effectively.

Evaluating the capacity of power and energy balance for cascaded H-bridge multilevel inverter using different PWM techniques

Abstract: Among the different multilevel topologies for inverters, cascaded H-bridge multilevel topology has been a good solution for high-power medium-voltage applications because of its modularity structure, voltage balancing, separated DC sources, harmonics reduction, reliability and lower stresses on switching devices. The most widely used pulse width modulation (PWM) techniques for cascaded H-bridge multilevel inverter are known as phase shifted and level shifted. This study presents a comparison of power and energy distribution (inter-phase and inter-bridge power and energy) of three-phase cascaded H-bridge multilevel inverter for the phase-shifted and the level-shifted carrier PWM (phase disposition). A detailed comparison of different PWM techniques with reference to total harmonic distortion in the output voltages, both cases using a filter and without filter are presented also in this study. Simulation for 11-level cascaded H-bridge inverter is carried out in MATLAB/SIMULINK and simulation results are presented.

Optimal accommodation and management of high renewable penetration in distribution systems

Abstract: The paper presents a new bi-level optimisation framework for optimal accommodation and operational management of wind power generation and battery energy storage system (BESS) simultaneously, aiming to maximise the renewable hosting capacity of distribution networks. A new objective function is suggested comprising of annual energy loss in feeders, reverse power flow into the grid, non-utilised BESS capacities, round-trip conversion losses of BESSs and node voltage deviation subjected to various system security constraints. An artificial-intelligence-based optimal management of BESS is proposed for effective control of high-renewable power generation. Due to the high investment and running costs of BESS, minimum storage capacity has been ensured in planning stage. In order to show the effectiveness of the proposed model, it is implemented on a benchmark test distribution system of 33-bus. Besides, various test cases are investigated and compared, which shows that the proposed optimisation model is promising.

Battery management system for Li-ion battery

Abstract: Li-ion batteries are widely used in the fields of electric vehicles and energy storage because of high energy density, low self-discharge rate, long cycle life, and wide operation temperature range. To ensure safety and prolong the service life of Li-ion battery packs, a battery management system (BMS) plays a vital role. In this study, a combined state of charge (SOC) estimation method and passive equilibrium control are mainly studied for lithium cobalt oxide batteries. A BMS experimental platform is designed, including both software programming and hardware. The experimental platform has the following functions: voltage and current measurements, SOC calculation, balance control, over charging and over discharging alarm and protection, battery status detection, liquid crystal display etc.

Load frequency control in multiple microgrids based on model predictive control with communication delay

Abstract: The time delay caused by the communication network will influence the load frequency control (LFC) of the power system including multiple microgrids. A multi-area power system consisting of multiple standard microgrids connected with tie lines is built in this study. Then the LFC equivalent model is constructed based on the proposed system considering the time delay. After that, a coordinated controller based on the model predictive algorithm is designed to deal with the load disturbances and the fluctuation of the wind power with communication delays in the LFC model. Case studies based on a multiple delays LFC system demonstrate the effectiveness of the proposed load frequency controller comparing with the conventional proportional integral load frequency controller.

Action recognition using vague division DMMs

Abstract: This study presents a novel human action recognition method based on the sequences of depth maps, which provide additional body shape and motion information for action recognition. First, the authors divide each depth sequence into a number of sub-sequences. All these sub-sequences are of uniform length. By controlling vague boundary (VB), they construct a VB-sequence which consists of an original sub-sequence and its adjacent sequences. Then, each depth frame in a VB-sequence is projected onto three orthogonal Cartesian planes, and the absolute value of the difference between two consecutive projected maps is accumulated to form a depth motion map (DMM) to describe the dynamic feature of a VB-sequence. Finally, they concatenate the DMMs of all the VB-sequences in one video sequence to describe an action. Collectively, they call them VB division of depth model. For classification, they apply robust probabilistic collaborative representation classification. The recognition results applied to the MSR Action Pairs, MSR Gesture 3D, MSR Action3D, and UTD-MHAD datasets indicate superior performance of their method over most existing methods.

Inertial response analysis of PMSG-based WECS with VSG control

Abstract: The inertial response of permanent magnet synchronous generator (PMSG)-based wind energy conversion system (WECS) with virtual synchronous generator (VSG) control (WECS-VSG) is analysed in this paper. The differences between WECS-VSG and synchronisation generator (SG) are focused. For SG, the input active power is constant during inertial response stage. However, due to the control coupling between VSG and the other control function of WECS, the VSG active power reference value will change during the same stage. In order to understand the problem physically, the effective inertia of the WECS-VSG is deduced from the perspective of output equivalence. Based on the analyses of effective inertia, the authors know that the control coupling in WECS-VSG will weaken the VSG inertial response ability. Therefore it is a must to improve the WECS-VGS control realisation in the future study. All the analyses in this paper are verified by simulation results.