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Virtual power plants (VPPs) are becoming critical parts of energy systems to increase renewable energy integration and to reduce the cost of electricity. To maximize the benefits to customers and VPP owners, the consumers' engagement is important for adding flexibility to the electricity load of the VPP. In this paper, the impact of customer contributions into a VPP energy management system through gamification is studied. To this aim, the contribution of customers within a realistic VPP of 67 dwellings in Western Australia is modelled. This model is included in a robust optimized procedure to maximize the profit of a VPP owner over a year. In this platform the uncertainties associated with renewable energy generation and market electricity are considered to find the optimum solution for the worst case scenario of uncertainties. The simulation results show that the gamified customer involvement has positive impacts on increasing the profit of the VPP.

The number of synchronous generators is anticipated to be reduced in the future power systems. Factors such as favorable government policies and market's preference are driving enormous deployment of renewables, while there is very little interest in replacing coal fired power stations by the same type of power generators. These have culminated in the increased deployment of asynchronous generators in power systems. While these types of generators are environmentally friendly, they perform poorly in maintaining power system strength. This paper investigates the impact of photovoltaic (PV) generator on fault level as an indicator of system strength of a network. Various technological options for system strength improvement are discussed. It shows that system fault level can be supported by independent control of synchronous condenser's voltage and reactive power control. The paper also formulates an analytical control approach and demonstrates its suitability on a modified 35-bus utility system.

In this paper, a hybrid power system is designed based on a game theory approach consisting of different components like wind turbines, PV panels and batteries in grid-connected mode. Nowadays, as energy problems and atmospheric pollution are increasing rapidly, green energy production is playing a vital contribution. The Game Theory-based approach Nash equilibrium has been used to achieve desired objective functions and to perform analysis to reach towards the goals. Game theoretical models of the noncooperative and cooperative game are considered, and wind turbines, PV panels and batteries are taken as players and the life-cycle income is chosen as payoffs. To obtain Nash equilibriums, a multi-objective particle swarm optimization algorithm has been developed in MATLAB. The sensitivity analysis is performed to validate the stability of Nash equilibriums for the price of electricity and discount rates. In the end, the outcomes of the proposed model are compared with recent research work to check the feasibility and validate the results.

This paper proposes a democratic current sharing controller for Modular Multi-parallel Rectifiers (MMR) system operating in Continuous Conduction Mode (CCM). A generalised small-signal AC model for n-number of parallel units is derived. Also, State-Space equations and transfer functions for the system are provided in this paper. A cascaded loops controller for the parallel units is utilised to ensure that the rectifiers' currents are balanced equally. A 60 kW system is modelled in MATLAB/Simulink to verify the proposed controller.

Frequency stability of most modern power systems has significantly deteriorated in the recent past due to the rapid growth of inverter interfaced renewable energy generation systems. Energy storage systems are being identified as a promising remedy to overcome such adverse effects. This paper investigates the dynamic response of a power system that has high renewable energy penetration and is also compensated by a large-scale energy storage system. The system dynamic response with a ternary pumped hydro storage, a liquid air storage and a battery energy storage system are studied and compared in this paper. Dynamic models for each storage system and their time domain responses are evaluated using a modified IEEE 14 bus test system. The paper produces an accurate methodology to simplify dynamic models and study the frequency response characteristics of large-scale ESS. Results show that the BESS has very fast frequency response compared to other two large-scale ESS. Fast and slow responsive hybrid model containing large-scale ESS may provide promising solution for storage-based grid ancillary services.

Distribution of keys in classical cryptography is one of the most significant affairs to deal with. The computational hardness is the fundamental basis of the security of these keys. However, in the era of quantum computing, quantum computers can break down these keys with their substantially more computation capability than normal computers. For instance, a quantum computer can easily break down RSA or ECC in polynomial time. In order to make the keys quantum resistant, Quantum Key Distribution (QKD) is developed to enforce security of the classical cryptographic keys from the attack of quantum computers. By using quantum mechanics, QKD can reinforce the durability of the keys of classical cryptography, which were practically unbreakable during the pre-quantum era. Thus, an extensive study is required to understand the importance of QKD to make the classical cryptographic key distributions secure against both classical and quantum computers. Therefore, in this paper, we discuss trends and limitations of key management protocols in classical cryptography, and demonstrates a relative study of different QKD protocols. In addition, we highlight the security implementation aspects of QKD, which lead to the solution of threats occurring in a quantum computing scenario, such that the cryptographic keys can be quantum resistant.

The cellular network standard is gradually stepping towards the 6th Generation (6G). In 6G, the pioneering and exclusive features, such as creating connectivity even in space and under water, are attracting Governments, organizations and researchers to spend time, money, effort extensively in this area. In the direction of intelligent network management and distributed secured systems, Artificial Intelligence (AI) and blockchain are going to form the backbone of 6G, respectively. However, there is a need for the study of the 6g architecture and technology, such that researchers can identify the scopes of improvement in 6G. Therefore, in this survey, we discuss the primary requirements of 6G along with its overall architecture and technological aspects. We also highlight crucial challenges and future research directions in 6G networks, which can lead to the successful practical implementation of 6G, as per the objective of its introduction in next generation cellular networks.

To enhance the wireless network performance, several new wireless standards, such as IEEE 802.11ac/ax, are introduced, where the objective is to increase the throughput in the network. However, Transmission Control Protocol (TCP) congestion degrades the network performance due to limited network capacity. The standard congestion control mechanism focuses primarily on acknowledgment (ACK) packets, which may result in inappropriate handling of congestion, and lead to the increase in packet drop rate and packet transmission delay. As a consequence, overall network efficiency is degraded. In this paper, we propose Intelligent Congestion control (ICon), a learning-based congestion control mechanism, that uses Deep Neural Network (DNN) to smartly predict the occurrence of congestion based on the present channel condition in the network and the last congestion window size (old window size). We use Signal-to-Noise Ratio (SNR) as the measurement of channel condition, and thus the impact of signal quality on the volume of transmitted packet is implicitly introduced in the prediction of congestion in ICon. For the generation of the dataset to train the proposed DNN, an intensive simulation is used. Through simulation, we analyze the performance of ICon, where it is noted that the proposed scheme significantly improves overall network performance.

This paper presents a comprehensive steady-state analysis for active front-end Modular Multi-parallel Rectifiers (MMR) including parasitic elements. Generalised mathematical representations for the system’s gain and conduction loss effect are provided. In addition, the impact of the inductor losses and the firing angle on the system’s performance is investigated. A60kW Simulink model is utilised to validate the proposed mathematical model and the system characteristics and operating modes.

Multi-agent-based smart grid applications have gained much attention in recent times. At the same time, information and communication technology (ICT) has become a crucial part of the smart grid infrastructure. The key intention of this work is to present a comprehensive review of the literature and technological frameworks for the application of multi-agent system (MAS) and ICT infrastructure usages in smart grid implementations. In the smart grid, agents are defined as intelligent entities with the ability to take decisions and acting flexibly and autonomously according to their built-in intelligence utilizing previous experiences. Whereas, ICT enables conventional grid turned into the smart grid through data and information exchange. This paper summarizes the multi-agent concept of smart grid highlighting their applications through a detailed and extensive literature survey on the related topics. In addition to the above, a particular focus has been put on the ICT standards, including IEC 61850 incorporating ICT with MAS. Finally, a laboratory framework concepts have been added highlighting the implementation of IEC 61850.

This paper proposes a peer-to-grid (P2G) energy trading combined with peer-to-peer (P2P) energy trading scheme based on a cooperative game theoretical technique to optimize sizes of the generation resources and battery, and achieve maximum payoff from a networked microgrid. The selected architecture consists of two microgrids in which both microgrids contain solar panels, wind turbines, and batteries to meet the requirements of the load. In the first stage, a game theory technique based on particle swarm optimization (PSO) method is used to find the optimum sizes of the generation resources and batteries considering the conventional P2G combined with P2P energy trading. In the second stage, considering two energy trading scenarios including P2G and P2G combined with P2P capability, the maximum payoffs of both microgrids are optimized and compared.

Microgrid (MG), a revolutionary concept in the energy infrastructure, plays an important role for the establishment of a resilient grid infrastructure. Since its emergence, it has evolved around a number of cutting edge technologies for its smooth operation and control. Among them multi-agent system (MAS) provides an intelligent and decentralized platform for the control of microgrid. This paper highlights the application of a MAS in an AC microgrid, including a detailed structure of microgrid, the communication interface between microgrid and multi-agent platform. A detailed small scale microgrid model has been simulated in MATLAB/SIMULINK environment, whereas the agent platform has been implemented in JADE (Java Agent Development Framework) platform. The MAS autonomously detects main grid outage and facilitates seamless transition from grid-connected mode to islanding mode; thus ensures overall smooth operation of the power network. Simulation results are presented to verify the effectiveness of the MAS based control system.

Short guard interval (SGI) is a new feature of the physical (PHY) layer in High Throughput Wireless Local Area Networks (HT-WLANs), like IEEE 802.11n/ac, where the value of the guard interval is set to 400ns instead of general 800ns. By reducing the overhead of extra idle time between transmissions of two successive symbols, SGI helps to enhance the average system throughput, compared to the guard interval of 800ns. However, the signal quality of the channel and high data traffic can affect the performance of SGI in a network. In a congested network scenario, channel interference and load of data transmission are increased; and consequently, inter-symbol interference becomes higher, which increases the rate of packet losses and thus, reduces average throughput in the case of SGI. In this paper, we design Thompson sampling based online learning mechanism, Smart Selector of Guard Interval (S2-GI), to select guard interval dynamically by considering the present channel condition. We analyze the performance of S2-GI through a thorough simulation in IEEE 802.11ac WLAN and we observe that S2-GI can enhance the network performance.

In today's world, the Internet of Things i.e IoT has possessed a great impact in our life. Almost everything from electrical to electronic gadgets have become a part of IoT due to which the data production and consumption have increased. Cloud computing due to its centralized architecture is not enough to handle this exponential growth of data it suffers from and thus certain limitations. Due to the drawbacks of cloud computing, a decentralized computing paradigm called fog computing was coined where computation of data used to happen near to the network edge. But due to some limitations of fog computing a similar type but an advancement over fog came into existence called edge computing which could resolve all the major drawbacks and challenges of cloud and fog to a large extent. Certain issues regarding high latency, high response time, high bandwidth cost, high energy consumption and high security threat are now resolved by edge computing to a large extent with lots of popular emerging fields and applications running on this concept. So the overall journey of this paper focuses on what exactly edge computing is and the need of edge computing in this growing world of data and how edge computing introduces the technology world with some of the emerging applications being widely used nowadays.

In an urban area, the degree of ambient noise and air pollution play a vital role in determining the quality of human life. The impact of these two pollutants is increasing day-by-day due to rapid urbanization. Although, creating real-time pollution maps and forecasting of pollution levels have been studied extensively, the contextual, spatio-temporal correlation between air and noise pollution has not been investigated thoroughly. This correlation is important to identify the characteristics of an urban area. In this paper, we have highlighted some aspects that are useful to identify a context from different pollutant data. To collect the noise data, we have developed an android based application “AudREC” that uses the inbuilt mobile micro-phone sensor. Moreover, for measuring air pollutants, we have used a ready-made “Flow” device that senses PM2.5 and CO 2 , etc. The initial outdoor experiments show the feasibility of the platform for recognizing contexts from air and noise pollution information.

The worldwide application and off-grid charging of plug-in electric vehicles (PEVs) are expected to grow in the next few years tremendously. However, uncoordinated PEV charging in low-voltage (LV) distribution networks can lead to grid problems such as transformer overloading and voltage unbalance. A potential solution and also a key challenge for PEV integration is the smart shifting of charging activities from peak hours to off-peak. In this background, this paper investigates the impacts of stochastic single-phase PEV charging based on time, rating and location of charging at residential houses on voltage and voltage unbalance factor (VUF). Additionally, Monte Carlo simulation is applied to describe the stochastic behaviour of PEVs. Different aspects like penetration level, PEV charging rate and location are investigated to better understand the impacts of PEVs on the LV distribution network. The impacts of uncoordinated and coordinated PEV charging on VUF and voltage profiles are simulated on a real Western Australian distribution network in the Perth solar city over 24 hours.

The installation of dispersed generation in on the rise in modern power systems, while the investments on conventional large generators are declining. The majority of distributed generators (DG) are non-synchronous type such as photovoltaic generators (PV), which are operated at near unity power factor. These non-synchronous generators lack the capability of reactive power support and hence the strength of the system to withstand disturbances is deteriorating. Therefore, capacity of network to host more PV units is posing a major challenge. Deterioration of system strength is revealed by decreasing fault levels and short circuit ratio (SCR) across the nodes of a network as a result of increasing penetration of PV units. Therefore, utilities have prescribed minimum values of short circuit ratio as an important minimum requirement for the connection of new solar PV generators. This paper investigates the impact of solar PV units’ connection on SCR of an electricity network. Appropriate size of reactive compensation is calculated for the improvement of SCR. The effectiveness of capacitive compensation on the improvement of SCR across the network is also demonstrated.

An LCL filter utilized in an active front end (AFE) converter can generate significant resonance, which can affect quality and stability of the system. Thus, a proper active or passive damping technique and/or a suitable controller is required to be designed for the converter. The duty cycle constraint—though inevitable in practical inverter systems—is often ignored in most of the existing literature. The effects of measurement noise on filter control system performance must be considered and evaluated under different conditions. Finally, almost all inverter control systems are implemented digitally using microcontrollers. Consequently, the effects of sampling on control system stability and performance should be evaluated as well. Thus, this paper presents stability analysis of an AFE converter-based filter design with a complete practical system configuration, including saturation and sampling blocks and measurement noises. Mathematical analysis and simulations have been carried out to validate the proposed method.

To meet the fast-growing energy demand and, at the same time, tackle environmental concerns resulting from conventional energy sources, renewable energy sources are getting integrated in power networks to ensure reliable and affordable energy for the public and industrial sectors. However, the integration of renewable energy in the ageing electrical grids can result in new risks/challenges, such as security of supply, base load energy capacity, seasonal effects, and so on. Recent research and development in microgrids have proved that microgrids, which are fueled by renewable energy sources and managed by smart grids (use of smart sensors and smart energy management system), can offer higher reliability and more efficient energy systems in a cost-effective manner. Further improvement in the reliability and efficiency of electrical grids can be achieved by utilizing dc distribution in microgrid systems. DC microgrid is an attractive technology in the modern electrical grid system because of its natural interface with renewable energy sources, electric loads, and energy storage systems. In the recent past, an increase in research work has been observed in the area of dc microgrid, which brings this technology closer to practical implementation. This paper presents the state-of-the-art dc microgrid technology that covers ac interfaces, architectures, possible grounding schemes, power quality issues, and communication systems. The advantages of dc grids can be harvested in many applications to improve their reliability and efficiency. This paper also discusses benefits and challenges of using dc grid systems in several applications. This paper highlights the urgent need of standardizations for dc microgrid technology and presents recent updates in this area.

The ambient environment conditions play a significant role in determining the actual quality of life in smart cities. Among the several pollutants that have impacted the environment compromising well being of the citizens is urban noise pollution. The prime reasons for the rise of noise pollution are imbalanced urbanization, unregulated increase in traffic and inorganic industrialization. In a bid to realize the concept of smart cities, it has become important to monitor this environment parameter for policy-making and planning for making cities livable and sustainable. The existing infrastructure for noise pollution monitoring is limited considering the coverage needed and is mostly provided by the government agencies. The advancement in mobile technology and the increasing number of mobile phone users has given an alternative in the form of participatory sensing using inbuilt microphones in mobile devices for ambient noise pollution monitoring. In these contexts, the proposed work NoiseProbe, intends to implement a participatory sensing framework by incorporating crowd-sensed data from mobile sensing to monitor city-scale noise pollution levels in real-time. It will use the aggregated information to understand the spatio-temporal characteristics of noise pollution, creating a meaningful visualization, as well as providing each user with information on their personal exposure to noise pollution. The initial outdoor experiments demonstrate that NoiseProbe can be a feasible platform for assessing the dynamics of noise pollution monitoring and providing fruitful information.

This paper targets to introduce a novel approach for capacity determination of a hybrid energy storage system (HESS) of a standalone microgrid with high PV penetration. The imbalance power between solar PV and load is decomposed to yield its frequency-time components using Empirical Mode Decomposition (EMD) technique. Statistical characteristics of the components are then matched against the attributes of the energy storage devices, namely the electrochemical battery and supercapacitors. This is to achieve the collective benefit of the two storage media in their unique power and energy handling capabilities. This integrated approach is utilized to smoothen the PV variations in the most cost-effective manner. Concurrently, a proposed design procedure is used to determine the storage capacities of the hybrid energy storage system to avoid penalty of un-met load.

Air and noise pollution are two major factors that determine the quality of life of the people living in cities. The prime reasons for the rise of air and noise pollution are due to imbalanced urbanization, unregulated increase in traffic and inorganic industrialization. These have resulted in compromising the well-being of the citizens. In this context, the concept of smart cities has been developed. They inherently have the ability to sense and respond to the challenges which characterizes regular cities with the help of embedded intelligence. It has become important to monitor the environmental parameters for policy-making, planning and for making smart cities livable and sustainable. In a bid to make a smart city, in this work, we have studied the spatio-temporal relationship between air and noise pollution in four different locations and have also evaluated the effect of noise in predicting Air Quality(AQ). Data acquisition has been done using customized, self-developed CO_2; NO_2; PM2:5, humidity, temperature and intensity of noise. To determine the relationship between air and noise pollution, we have used Pearson correlation. Results show a strong association between the two types of pollution. For predicting the air quality, the impact of noise pollution as a feature has been investigated using three different machine learning models which are Decision Tree, Random Forest and K-Nearest Neighbors. When applicable, the results show that if noise pollution is used as a feature, we get a prediction accuracy of upto 95% which is an improvement of 5% on an average

Construction of a backbone in a wireless network is one of the most important issues. It is far more difficult to construct a backbone in a network consisting of selfish nodes rather than altruistic nodes, as selfish nodes would like to avoid any participation in constructing the backbone. In this paper, we have calculated the selfish nature of the nodes based on some predefined parameters and made the most selfish node to participate first in the construction of the backbone. Our proposed algorithm, Recursive Backbone formation in Selfish wireless Networks $(ReBSN)$, constructs the backbone dynamically by using selfish nodes whenever any node wants to transmit some data to another node. At the end of a transmission, the backbone exists only for some short predefined time period and again it is formed only when a node wants to initiate its data transmission. Thus, this approach is free from the overhead of maintaining a backbone. We have analyzed the performance of ReBSN through simulation, where it can be noted that ReBSN can improve the network performance significantly.

Depression is a major debilitating disorder which can affect people from all ages. With a continuous increase in the number of annual cases of depression, there is a need to develop automatic techniques for the detection of the presence and extent of depression. In this AVEC challenge we explore different modalities (speech, language and visual features extracted from face) to design and develop automatic methods for the detection of depression. In psychology literature, the PHQ-8 questionnaire is well established as a tool for measuring the severity of depression. In this paper we aim to automatically predict the PHQ-8 scores from features extracted from the different modalities. We show that visual features extracted from facial landmarks obtain the best performance in terms of estimating the PHQ-8 results with a mean absolute error (MAE) of 4.66 on the development set. Behavioral characteristics from speech provide an MAE of 4.73. Language features yield a slightly higher MAE of 5.17. When switching to the test set, our Turn Features derived from audio transcriptions achieve the best performance, scoring an MAE of 4.11 (corresponding to an RMSE of 4.94), which makes our system the winner of the AVEC 2017 depression sub-challenge., 8 pages, 1 figure

Human activity recognition is a challenging research area having possible implementation in various aspects of our life. It can be beneficial in automation, surveillance, vigilance, assisted living, etc. Most of the recent works done in this area have focused on identifying single-person activities. But in real scenario, many of these activities are performed in groups. The kind of technologies used are combination of computer vision and wearable devices. The use of such techniques may interfere with one's privacy. In this context, our work intends to identify group activities using a non-invasive, non-intrusive, non-wearable sensor based system. The proposed system identifies activities such as walking, standing, sitting, gathering etc. comprising a group of people. We have used customized multiple ultrasonic sensor grids for data capturing. For classifying primary activities, decision tree technique has been used. Other concurrent group activities were identified using Hidden Markov Model (HMM). The proposed system achieved an accuracy up to 90% in most of the cases.

This paper introduces a control technique for proportional load sharing in a DC microgrid during both islanded and utility connected modes. The DC microgrid is connected to a utility system through an interlinking voltage source converter and a unidirectional DC-DC converter. The DC-DC converter draws the desired amount of power from the utility, while the interlinking converter holds the DC bus voltage. The DERs in the DC microgrid are operated under droop sharing using DC-DC converters. Each DC-DC converter is equipped with a state feedback with integral controller that can regulate its output voltage for a wide range of input voltage fluctuation and load variation. Simulation studies are conducted on PSCAD/EMTDC to validate the proposal.

Wind Power plays a major role in both large utility grids and small microgrids due to a wide range of socio-economic benefits. Due to this reason, current research has an emerging trend to enhance its reliability and usability. Highly random nature of the wind speed and direction leads to having a poor accuracy of wind power forecasting and thereby poor reliability, increased cost and reduced efficiency of electrical systems. Most updated studies are focused mainly on wind speed, and their prediction errors are above the industry expectations. In this paper, both the wind speed and wind direction are analyzed to develop a statistical model based forecasting technique. This paper uses an Autoregressive Integrated Moving Average method to build the estimating model for wind measured in Western Australia to yield the forecasted values. The resultant model can be used to improve the system reliability, quality of the wind power generation system.

Knowledge of the complete clinical history, lifestyle, behaviour, medication adherence data, and underlying symptoms, all affect the treatment outcomes. Collecting, analysing and using all these data, while treating a patient can often be very challenging. A doctor can spend only a limited time with a patient. This time is often not enough to learn about all the lifestyle and underlying conditions of a patient's life. Often patients are asked to maintain diaries of their daily activities. Diaries can help to improve adherence by increasing the consciousness of the patients, and can also serve as a way for the doctors to validate this adherence. However, diaries can be cumbersome to parse, and hence increase the task burden of the doctor. In this paper we demonstrate that automatic analysis of diaries can be used to predict the stress level of the diary writers with an F-measure of 0.70.

This paper proposes a control strategy for sharing load requirements in a low voltage islanded microgrid with converter interfaced distributed energy resources. In high voltage lines with large X/R ratio, real and reactive power are decoupled and thus conventional droop control can effectively share the power needs of the load. However, the same strategy cannot be applicable in dominantly resistive remote microgrids where, X/R ratio is very small. In order to ensure accurate sharing of load power, this paper adopts cut in contrast of conventional angle droop control, where active power sharing is achieved by controlling voltage and reactive power by angle droop method. In addition, virtual resistive output impedance is utilized to effectively decouple real and reactive power. Moreover, this strategy includes influence of line impedance in load power sharing and offers an added advantage of effective overall system damping. Third harmonic injection based pulse width modulation scheme is used for each distributed generators(DGs), which provides better utilization of DC link voltage and suppress neutral currents under unbalanced conditions.

Fine needle aspiration cytology is commonly used for diagnosis of breast cancer, with traditional practice being based on the subjective visual assessment of the breast cytopathology cell samples under a microscope to evaluate the state of various cytological features. Therefore, there are many challenges in maintaining consistency and reproducibility of findings. However, digital imaging and computational aid in diagnosis can improve the diagnostic accuracy and reduce the effective workload of pathologists. This paper presents a deep convolutional neural network (CNN) based classification approach for the diagnosis of the cell samples using their microscopic high-magnification multi-views. The proposed approach has been tested using GoogLeNet architecture of CNN on an image dataset of 37 breast cytopathology samples (24 benign and 13 malignant), where the network was trained using images of ~54% cell samples and tested on the rest, achieving 89.7% mean accuracy in 8 fold validation.

Human activity recognition is an important problem in health care, ambient-assisted living, surveillance-based security, etc. and has crucial applications in smart environment. A non-invasive, automated system for monitoring human activity using array of heterogeneous ultrasonic sensors has been proposed in this work. Ultrasonic sensors are widely used for distance measurement in many applications. In the proposed system experiments have been conducted using ten volunteers in a controlled laboratory environment. The data collection unit has two kinds of setups of ultrasonic sensors: the former with five HC-SR04 sensors, and the latter with four HC-SR04 ultrasonic sensors and an LV-MaxSonar-EZ0 sensor. The proposed method is found capable of detecting standing, sitting and falling of a person, and also the movements in different directions. Based on the collected data, we have performed classification analysis using multiple machine learning algorithms. The experimental results show 81% to 90% correct detection of different activities of the volunteers.

Microscopic examination of stained tissue sections obtained from biopsies forms the foundation of pathologically diagnosing diseased organs. Although offering a multi-scale view of the system pathology, interpretation is often subjective. Inter- and intra-observer variation in reporting due to limitations in tissue staining methods accounts for this subjectivity. Variation in stain uptake often leads to spectral overlap, variation in image intensities thus affecting the spectral signatures of imaged section and challenging computer assisted diagnosis. Stain density estimation techniques often referred to as digital stain separation have been introduced to overcome such limitations through quantification of amount of local stain uptake by a tissue. This is achieved by decorrelating the spectrally spread signal received by the color imaging sensor. Both blind and unblinded methods have been proposed with each having its own benefit. We focus on devising a framework for robust estimation of color deconvolution matrices as the basis for unblinded method. Here we propose a generalized framework using the Beer-Lambert intensity decay law for relating the stain uptake at a region to the resulting color and impose a Maxwellian chromaticity based regularization factor for solving the deconvolution matrix. The algorithm is trained and tested over different combinations of HE, VG and PAS stained cervical biopsy samples. The performance is validated using a 6-fold cross validation technique for each stain family and error in estimation measured across folds using MSE of stain uptake measurement. This generalized framework can be appropriately modified to other histopathological and immunohistochemical compound staining methods in brightfield microscopy.

Advancement in communication technology and its application to power systems have introduced a new era in power system scheduling. Demand Side Management (DSM) is one of the main outcomes of this combination. In this paper, a droop-based hierarchical control strategy has been proposed to execute automatic Demand Dispatch (DD). When the frequency of the system falls/rises below/above rated level, Balancing Authority (BA) issues droop-based signal to the aggregators to check the feasibility and availability of DD according to the capability of aggregated loads. Then, these signals will be sent to the Appliance Management Unit (AMU). According to the availability, priority, and features of appliances, the AMU sends these commands in form of deferral or interruptible signals to appliances. At the time of DD events, the proposed AMU algorithm keeps the total household power consumption within the required limit to maintain the system frequency at nominal level. A wide variety of residential loads has been considered in the DD applications. It will be demonstrated that the proposed AMU algorithm is capable of maintaining the total demand of a house below the limit determined by the BA. In the second part of this paper, the necessity of renewable energy resources and battery storage system to prevent load rebound is highlighted.

Recent progress in power electronic converters has increased the number of distributed energy sources to the grid. These converters act as a source of harmonics which connected with other loads affect the stability of power grid. Parallel operation of converters is common in power grid depends on the power sharing needs and current rating of power semiconductor devices. Though, these connections improves power handling capability of grid, the dynamic interaction between converters can affect stability of system. This paper addresses the stability issues in parallel connected, high frequency voltage controlled converters with an output LC filter. Studies on interactions between control loops verifies that, harmonic instability introduced by parallel converters can't be stabilized by traditional PI controllers in real power control loop. A new damping algorithm is introduced to suppress these harmonic oscillations caused by the interaction of controllers in grid connected converters.

Coupling remote microgrids (MGs) is a promising technique to handle power deficiency in such systems. However, once the shortfall is removed, the coupled MGs (CMG) should isolate to carry on their operation autonomously. Due to the non-zero power still flowing through the tie-line, however, considerable voltage spikes may be imposed on the interconnecting static switch (ISS) during the isolation that may damage the device. This paper proposes a practical decentralized approach to efficiently alleviate the mentioned transients, facilitating a successful MG isolation procedure. Two auxiliary controllable loads (ACL) are to be considered at two sides of the ISS. When the necessity of applying the isolation action is detected, the ISS controller commands the ACL on the supporting MG side to connect. A droop-based scheme is formulated to derive the load should be demanded by the ACL such that the power passing through the ISS drops to zero, preventing any spikes across it at the opening moment. Once the isolation action is accomplished, the controller disconnects the ACL. Efficiency of the proposed algorithm is validated through various cases simulated using PSCAD/EMTDC.

Photoplethysmography (PPG) is a simple, unobtrusive and low-cost technique for measuring blood volume pulse (BVP) used in heart-rate (HR) estimation. However, PPG based heart-rate monitoring devices are often affected by motion artifacts in on-the-go scenarios, and can yield a noisy BVP signal reporting erroneous HR values. Recent studies have proposed spectral decomposition techniques (e.g. M-FOCUSS, Joint-Sparse-Spectrum) to reduce motion artifacts and increase HR estimation accuracy, but at the cost of high computational load. The singular-value-decomposition and recursive calculations present in these approaches are not feasible for the implementation in real-time continuous-monitoring scenarios. In this paper, we propose an efficient HR estimation method based on a combination of fast-ICA, RLS and BHW filter stages that avoids sparse signal reconstruction, while maintaining a high HR estimation accuracy. The proposed method outperforms the state-of-the-art systems on the publicly available TROIKA data set both in terms of HR estimation accuracy (absolute error of 2.25 ± 1.93 bpm) and computational load.

This paper proposes a self-healing strategy for two neighboring islanded microgrids (MGs) that are connected together through a normally open interconnecting static switch (ISS). Each MG is expected to support the other during power deficiencies, which is facilitated by the developed self-healing agent. This agent operates based on either a centralized approach, when data communication system is available, or a decentralized approach, when data communication system does not exist or is temporarily unavailable. The necessity and possibility of the MGs interconnection or isolation is determined based on instantaneous power generation of the distributed energy resources in both MGs (in the centralized approach) and local frequency measurements at either side of ISS (in the decentralized approach). The dynamic performance of the MGs with the proposed strategies is evaluated by PSCAD simulation studies. Furthermore, the stability of the system of coupled MGs is investigated through small-signal stability and modal analyses in MATLAB.

One of the most effective and cheapest strategies to integrate the fast-increasing number of solar-based generators is by forecasting their power output to schedule power dispatch, energy storages, manage backup generators, and compensate for any fluctuations of solar power. In particular, forecasting the insolation is invaluable to predict the power generated by photovoltaic arrays. An essential initial step towards forecasting insolation under realistic weather conditions is to precisely model the clear-sky irradiance. The reduction of insolation due to other factors such as cloud cover, aerosol, or any other cause of light-scattering can be taken into account by superimposing each relevant model on top of the clear-sky insolation model. Since the precision of the irradiance forecast is critical to the effective control of such sources of power, this paper compares several already available empirical global horizontal irradiance models to determine which models fit best for Western Australia.

Three types of offshore wind power collection topologies are studied for DC power systems in this paper. Permanent Magnet Synchronous Generator (PMSG) based Wind Energy Conversion System (WECS) is considered and the Optimal Power Control (OPC) is used for the Maximum Power Point Track (MPPT) control. The failure of a wind power unit is regarded as the fault condition in this paper. Voltage control strategies are proposed for series and series-parallel power collection topologies when such a fault happens. Input and output voltage references of the Grid-Side Converter (GSC) are modified for series power collection systems on fault conditions. For series-parallel collection systems, power switches are employed between wind power branches and different voltage control effects with different switch options are discussed. Simulation studies are conducted base on PSCAD/EMTDC to validate the proposed control strategies.

The degree of proliferation of Distributed Energy Resources (DERs) and application of power electronics appliances are in demand. A microgrid can contain variety of sources such as diesel generator, wind turbine, PV arrays, micro-turbine and fuel cell. Usually these sources share power where the system frequency changes depending on the power supplied. On the other hand, the microgrid can contain harmonic loads that can affect the performance of the synchronous generators. These deleterious effects of distorting loads can be corrected using a distribution static compensator (DSTATCOM). In this paper, an isochronous control based droop strategy is employed that forces the microgrid to operate at the base frequency of 50 Hz. Therefore the DSTATCOM can be operated at 50 Hz. A closed-loop space vector pulse width modulation (SVPWM) technique is used for the DSTATCOM control. The scheme is validated through the extensive PSCAD/EMTDC simulation studies.

In this paper, the interconnection of islanded DC microgrids are discussed. The interconnection increases the power supply reliability since a power shortfall in one of the microgrids can be supplied by the others. The islanded DC microgrids are interconnected through a bi-directional DC-DC converter. A high frequency transformer isolation is used in the converter. For interconnecting more than two microgrids, a multiple winding transformer is used. The interconnected system is studied for both equal and unequal capacity DC microgrids. A detail description of the interconnection technique is provided with simulation results and illustrations.

It is expected that a microgrid (MG) can supply its local loads independently; however the load and generation intermittency may lead an MG to experience overloading. This problem can be mitigated by coupling the overloaded MG to another neighboring MG that has surplus power. It is a challenge to select a suitable MG within a distribution network composed of several islanded MGs, to be coupled to the overloaded MG. This paper presents a new decision-making-based method to select the suitable alternative. This selection is based on different criteria including the available surplus power, reliability, supply security, power loss, electricity cost and CO2 emissions in the alternative MGs. Moreover, the frequency and voltage deviation in the system of coupled MGs are considered in the selection. The performance of the developed method is evaluated within a cloud theory-based probabilistic analysis in MATLAB.