Multi-source System Research Articles

Intelligent Clinical Decision Support.

Early recognition of pathologic cardiorespiratory stress and forecasting cardiorespiratory decompensation in the critically ill is difficult even in highly monitored patients in the Intensive Care Unit (ICU). Instability can be intuitively defined as the overt manifestation of the failure of the host to adequately respond to cardiorespiratory stress. The enormous volume of patient data available in ICU environments, both of high-frequency numeric and waveform data accessible from bedside monitors, plus Electronic Health Record (EHR) data, presents a platform ripe for Artificial Intelligence (AI) approaches for the detection and forecasting of instability, and data-driven intelligent clinical decision support (CDS). Building unbiased, reliable, and usable AI-based systems across health care sites is rapidly becoming a high priority, specifically as these systems relate to diagnostics, forecasting, and bedside clinical decision support. The ICU environment is particularly well-positioned to demonstrate the value of AI in saving lives. The goal is to create AI models embedded in a real-time CDS for forecasting and mitigation of critical instability in ICU patients of sufficient readiness to be deployed at the bedside. Such a system must leverage multi-source patient data, machine learning, systems engineering, and human action expertise, the latter being key to successful CDS implementation in the clinical workflow and evaluation of bias. We present one approach to create an operationally relevant AI-based forecasting CDS system.

Renewable sources-based automatic load frequency control of interconnected systems using chaotic atom search optimization

This paper proposes an improved form of chaotic based atom search optimization (IASO) algorithm by adapting one-dimensional (1D) chaotic map (tent, sine and logistic) to improve the search ability by intensifying the exploration and exploitation phase. The IASO avoids premature convergence and trapping into local optima. Initially, the proposed IASO is validated using a classical benchmark function and its performance is compared with ASO algorithm. Test results indicate that the proposed algorithm outperforms in terms of mean, standard deviation, and best values. Further, the proposed technique is used to design the parameters of fractional-order proportional integral derivative controller for automatic load frequency control (ALFC) of multi-area, multi-source hybrid power system (HPS) by minimizing the integral time absolute error. The results obtained show that the proposed control scheme improves the frequency response of the system by 48 %, 70 %, 15 % and 69 % in terms of settling time, peak undershoot, steady state error value and control effort, respectively compared to ASO. Moreover, the sensitivity analysis is carried out by considering ±25 % variation in HPS parameters and the real-time applicability is tested with Malaysian meteorological data of solar radiation and wind speed variation. These analysis indicates that the transient oscillations are damped out with minimum settling time and the system regains to stable operating conditions. Further, the evaluation of transient and steady-state performance indices shows that the tent map-based IASO is found to be more efficient for obtaining the optimal solution in solving the ALFC problems. In addition, the stability of the system is analysed by approximating the fractional-order transfer function based on the oustaloup filter in frequency domain.

Review of microgrid protection strategies: current status and future prospects

A microgrid is a developed form of a distribution system, which is integrated with a set of different types of distributed generation (DG) to supply local demand. In spite of that microgrids have many advantages as they increase reliability, raise efficiency, decrease feeder losses and voltage sag correction. However, there are many technical challenges faced, one of them is the protection of microgrid. Conventional protections have been made for radial distribution systems configuration. Where supplying source has one direction and the power flow is defined. The DG penetration converts the distribution network to a multi sources system causes a bidirectional power flow. Also, the most of DG uses a DC to AC converter which limit the fault current level. Therefore, a suitable protection scheme for microgrid ought to be designed to protect a microgrid from any disturbances may occur for both modes of operation grid-connected and islanded. The purpose of this paper is to summarize the challenges and problems facing microgrid protection. As well as the most strategies to date are presented with a discussion of their basic principles of operation to solve these problems. Finally, some conclusions and suggestions for microgrid protection in the future are presented.

Joint Performance Analysis of Ages of Information in a Multi-Source Pushout Server

Age of information (AoI) has been widely accepted as a measure quantifying freshness of status information in real-time status update systems. In many of such systems, multiple sources share a limited network resource and therefore the AoIs defined for the individual sources should be correlated with each other. However, there are not found any results in the literature studying the correlation of two or more AoIs in a status update system with multiple sources. In this work, we consider a multi-source system sharing a common service facility and provide a framework to investigate joint performance of the multiple AoIs. We then apply our framework to a simple pushout server with multiple sources and derive a closed-form expression for the joint Laplace transform of the AoIs in the case with independent M/G inputs. We further show some properties of the correlation coefficient of AoIs in the two-source system.

Research on Optimization Model of Multisource Traffic Information Collection Combination Based on Genetic Algorithm.

In order to reduce the excessive use of multisource traffic information collection system, a multisource traffic information collection combination optimization mode is proposed based on genetic algorithm in this paper. This model is mainly used to analyze the traffic management data in the city. According to the collected data information, the characteristics of the traffic equipment can be effectively analyzed. Basing on the market demand and supply relationship, the multisource traffic information collection combination optimization model is used to complete the reorganization and optimization of the traffic information in this paper, to acquire the main convolution feature variables of the model. The data information combination processing is performed according to the acquired feature variables, and the genetic algorithm is used to adjust the multisource traffic information. During the process of information fusion data analysis, the multisource traffic information clustering and fuzzy constraint control can be performed effectively to realize the optimization of the team's traffic information collection combination. Finally, the simulation results show that the method proposed in this paper is more accurate in realizing the optimization process of multisource traffic information collection and combination and has a better degree of information fusion.

Hybrid MPC-Based Automatic Generation Control for Dominant Wind Energy Penetrated Multisource Power System

This paper presents hybrid model predictive control-based automatic generation regulator design for dominant wind energy penetrated multisource power system. The other power generation sources hydro and thermal are also considered in each area. The proposed AGC regulator is designed for each independent area considering only local power system states to ensure the stability of closed-loop system under small perturbation. The performance of the proposed AGC regulator has been validated for 2% step load perturbation. Furthermore, system parameter is varied to 20% of nominal value to assess the robust performance of the regulator. Performance evaluation between the hybrid MPC, conventional MPC, and traditional AGC regulators considering the dominant participation of the DFIG wind turbines is presented to demonstrate the superior performance of the hybrid MPC AGC regulator.

Uncertainty Measure for Multisource Intuitionistic Fuzzy Information System

Multisource information systems and multigranulation intuitionistic fuzzy rough sets are important extended types of Pawlak’s classical rough set model. Multigranulation intuitionistic fuzzy rough sets have been investigated in depth in recent years. However, few studies have considered this combination of multisource information systems and intuitionistic fuzzy rough sets. In this paper, we give the uncertainty measure for multisource intuitionistic fuzzy information system. Against the background of multisource intuitionistic fuzzy information system, each information source is regarded as a granularity level. Considering the different importance of information sources, we assign different weights to them. Firstly, the paper proposes an optimal source selection method. Secondly, we study the weighted generalized, weighted optimistic, and weighted pessimistic multigranularity intuitionistic fuzzy rough set models and uncertainty measurement methods in the multisource intuitionistic fuzzy information system, and we further study the relationship between the three models and related properties. Finally, an example is given to verify the validity of the models and methods.

Automatic Generation Control of a Future Multisource Power System Considering High Renewables Penetration and Electric Vehicles: Egyptian Power System in 2035

Egypt aims to diversify electricity generation sources and targets 42% of its power capacity through different renewable energy sources (RESs) by 2035. Such an increased share of RESs will make grid operation and control more tedious and may have a negative impact on power system frequency stability. Therefore, this paper is the first study that studies the automatic generation control of future multi-source power systems (e.g., Egyptian Power System (EPS) in a future 2035 scenario) considering high RESs penetrations and electric vehicles (EVs). The RESs in this future scenario include photovoltaic plants, wind generation plants, concentrated solar power plants, and hydropower plants. The EPS also contains other traditional power plants based on fossil fuels (i.e., coal, oil, and natural gas) and nuclear power plants. Moreover, this study investigates the effect of participation of EVs in enhancing the frequency stability of the future-scenario EPS. Furthermore, this study proposes a fractional-order proportional integral derivative (FOPID) controller for load frequency control (LFC), and its parameters are tuned using RUNge Kutta optimizer (RUN), which is a new optimization algorithm. To assess the FOPID controller performance, it was compared with a proportional-integral-derivative controller, proportional-integral controller, and integral controller. The results showed the positive effect of EVs’ participation in frequency regulation of the future-scenario EPS, the effectiveness of RUN optimizer in LFC application, and the superior performance of the FOPID controller over its peers of controllers used in the literature in improving frequency stability through reducing frequency deviations caused by different disturbances and under various power system conditions.

Multivariable Coordinated Nonlinear Gain Droop Control for PV-Battery Hybrid DC Microgrid Access System via a T-S Fuzzy Decision Approach

The PV-Battery hybrid DC microgrid is an important structural form for distributed renewable energy microgrid applications. This paper focuses on improving the access utilization rate of PV-Battery energy and enhancing the access stability of the DC bus voltage. Firstly, based on the voltage droop control method for multi-source access system, the relationship between the power margin of PV-Battery energy and the regulation of DC bus voltage deviation is analyzed. Then, a multivariable T-S fuzzy decision approach is used to design a nonlinear virtual resistance-based voltage droop gain coefficient function, which achieves a highly adaptive coordinated distribution of PV-Battery power as well as supports DC bus voltage stabilization. The T-S fuzzy input variables include the DC bus voltage deviation of the access point, the state of charge of the battery, and the PV sunlight intensity. Finally, the simulation system is built employing MATLAB/Simulink, and the feasibility and effectiveness of the proposed strategy are verified through multi-scheme simulations.

An Optimized Multisource Bilinear Convolutional Neural Network Model for Flame Image Identification of Coal Mine

Underground fire monitoring is an important tool to improve coal mine production safety. In this paper, a multi-source information identification method based on bilinear convolutional neural network (B-CNN) is proposed, which consists of construction of multi-source image acquisition system, B-CNN and integrated decision making based on multi-source B-CNN. Aiming at the problem that Softmax loss function based on the gradient descent in B-CNN is easy falling into the local optimum, an improved Grasshopper Optimization Algorithm (GOA) is proposed to optimally selected two parameters of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\boldsymbol {W}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\boldsymbol {\theta } $ </tex-math></inline-formula> ; the method of initial solution generation based on sine mapping and the method of accepting bad solution with certain probability are respectively adopted. In order to solve high computational complexity in the stage of model training and integrated recognition by multi-source B-CNN, an image feature preprocessing method is proposed in this paper. Several feature vectors of color feature, shape feature and texture feature of the collected image are extracted and used as input vectors of B-CNN to complete model training and integrated recognition. In simulation experiments, firstly, four Benchmark functions are used to verify the performance of the improved GOA; then, by scaling, expanding and rotating the image to simulate the results of image acquisition at multiple positions and angles, different information sources can be formed to complete the integrated recognition by B-CNN. Three performance indexes of Accuracy, Precision and Recall are used to evaluate the simulation result of different comparative models, which show that the proposed method has better recognition effects.

Reconstruction of oil charging history in the multi-source petroleum system of the Beidagang buried-hill structural belt in the Qikou Sag, Bohai Bay Basin, China: Based on the integrated analysis of oil-source rock correlations, fluid inclusions and geologic data

The Beidagang buried-hill structural belt (BBHSB), located in the central part of the Qikou Sag, Bohai Bay Basin, hosts a multi-source and structurally complex petroleum system, in which the interpretation of oil-source relationships and the definition of hydrocarbon accumulation features are challenging due to the highly variable composition of oils and complex oil migration pathways. In this study, we use biomarkers, fluid inclusions, and geologic data to address the complexities of petroleum system analysis in BBHSB. Based on hierarchical cluster analysis, four genetic groups of oils (A, B, C and D) with distinct molecular compositions were identified. Group A, B, and C oils are originated from the source rocks in the third member (Es 3 ) of the Eocene Shahejie Formation (Es), the lower (Es 1 x ) section of the first member (Es 1 ) of Es, and the middle (Es 1 z ) section of Es 1 , respectively. Group D oils are mixed oils contributed by the source rocks of Es 1 x and the second member (Es 2 ) of Es. Bounded by geological settings, the charging histories of the four groups of oils were different. Fluid inclusion analysis suggests that Group A oils have two charging stages, i.e., the late Oligocene and the Pliocene-present. During the first stage, Group A oils mainly accumulated in Es 3 with relatively low maturity. During the second stage, the Group A oils vertically migrated along the fault in a large scale, resulting in its accumulation in the middle-shallow formations near the fault. Due to the late maturity of source rocks, Group B, C, and D oils have one accumulation stage that occurred during the Pliocene-present. Due to the blocking effect of the reverse drag anticline on the migration of oils to the fault, the accumulation of Group B and C oils were limited to inner source-layers and the distribution of Group D oils in the northeastern BBHSB was limited in Es 1 x . • The crude oils can be separated into four genetic groups (A, B, C, and D) with distinct sources. • Group A oils have two charging stages and the other three group oils have only one. • Constrained by geological conditions, the four group oils have different migration features. • The charging history of the four groups of oils has been constructed.

Study of the configuration of multi-source energy harvesting systems based on piezoelectric nanogenerator as a clean energy harvester

This paper presents the connection configuration of a multi-source piezoelectric transducer to extract energy from ambient mechanical forces to power up electronic devices. The configurations are optimized by applying a full-bridge rectifier (FBR) as an interface circuit to complete alternating to direct current (AC-DC) transformation before powering up any electronic devices. The FBR is varied in Silicon (Si-FBR) and Schottky (So-FBR) diodes to compare which one is more efficient. Six pieces of 35 mm piezoelectric transducer, PT, are connected in parallel and series connection then pressed under 20 N periodic force. The study shows that the configuration types of the multi-source PT have different results in harvesting mechanical energy. Experimental results show the maximum power can be harvested from six PTs in series for one and six Si-FBRs are 440 and 500 µW, respectively and for So-FBR obtained 150 and 730 µW. In a parallel configuration, maximum power can be harvested from six PTs for one and six Si-FBRs are 440 and 1050 µW, respectively and for So-FBR obtained 780 and 902 µW.

Equilibrium Optimizer and FLC of Two Area Load Frequency control with Multi-Source Generators System

Equilibrium Optimizer and FLC of Two Area Load Frequency control with Multi-Source Generators System

Multi-source renewable energy exchange management system for off-grid home load

Multi-source renewable energy exchange management system for off-grid home load

Analysis of tilt integral derivative controller-based automatic load frequency control of multi-area multi-source system

Analysis of tilt integral derivative controller-based automatic load frequency control of multi-area multi-source system

Analysis of tilt integral derivative controller-based automatic load frequency control of multi-area multi-source system

Analysis of tilt integral derivative controller-based automatic load frequency control of multi-area multi-source system

Application of Flower Pollination Algorithm for Solving Complex Large‐Scale Power System Restoration Problem Using PDFF Controllers

Automatic Generation Control (AGC) in modern power systems is getting complex, due to intermittency in the output power of multiple sources along with considerable digressions in the loads and system parameters. To address this problem, this paper proposes an approach to calculate Power System Restoration Indices (PSRI) of a 2‐area thermal‐hydro restructured power system. This study also highlights the necessary ancillary service requirements for the system under a deregulated environment to cater to large‐scale power failures and entire system outages. An abrupt change in consumer load demands and disturbances in any control region (area) of a multiarea (interrelated) system causes severe fluctuations in frequency and interarea power exchanges. However, simple Proportional and Integral (PI) controllers are most prevalent in the literature to effectively resolve AGC issues, while its integral gain is smaller due to the larger overshoot in transient performance. Therefore, an attempt has been made with a novel control strategy, known as the pseudoderivative feedforward with feedback (PDFF) controller, is developed to keep the interarea power exchanges and the frequency to the specified limits after load changes. A PDFF controller is designed and implemented using the flower pollination algorithm (FPA) to obtain optimal dynamic performance for different types of potential power flows in a restructured power system under investigation. The proposed PDFF controller localizes the zero at an optimal place that reduces the rise time of the step‐response to reduce the excessive overshoot and gives much better dynamic performances as compared to the PI control structure. The Integral Square Error (ISE) is considered as a performance criterion to derive the optimized gain of the PDFF control structure using FPA. Different PSRI are computed based on the transient response of the 2‐area deregulated multisource system and different restoration measures to be taken are also discussed. The simulation results clearly show that the proposed approach is very powerful in decreasing the frequency and tie‐power digressions under different load perturbations.

Protecting consumers in digitized and multi-source energy systems

ABSTRACT This paper investigates if the consumer risks of digitized and multi-source energy systems are capable of being addressed under existing energy consumer protection frameworks. Two challenges are identified. First, under existing frameworks the decision about which entities are subject to consumer protection obligations is often binary and may not easily accommodate new energy products, business models, or supply arrangements. Second, the highly prescriptive and detailed nature of current frameworks are ill-suited to the changes occurring in energy markets that require policymakers to develop rules to mitigate consumer risks arising from a rapidly expanding group of products, sources and technologies. To address these challenges, we propose that frameworks adapt to be based around baseline rights for all consumers irrespective of how they source, manage, or consume energy. We also argue for greater reliance on co-regulation to develop, monitor, and enforce a series of codes to protect consumers from the risks of new energy products and services.

Microgrid protection strategies: current status and future prospects

A microgrid is a developed form of a distribution system, which is integrated with a set of different types of distributed generation (DG) to supply local demand. In spite of that microgrids have many advantages as they increase reliability, raise efficiency, decrease feeder losses and voltage sag correction. However, there are many technical challenges faced, one of them is the protection of microgrid. Conventional protections have been made for radial distribution systems configuration. Where supplying source has one direction and the power flow is defined. The DG penetration converts the distribution network to a multi sources system causes a bidirectional power flow. Also, the most of DG uses a DC to AC converter which limit the fault current level. Therefore, a suitable protection scheme for microgrid ought to be designed to protect a microgrid from any disturbances may occur for both modes of operation grid-connected and islanded. The purpose of this paper is to summarize the challenges and problems facing microgrid protection. As well as the most strategies to date are presented with a discussion of their basic principles of operation to solve these problems. Finally, some conclusions and suggestions for microgrid protection in the future are presented.

Combined Voltage and Frequency Control of Multiarea Multisource System Using CPDN-PIDN Controller

This paper discusses a multiarea multi-source system in combined automatic load frequency control (ALFC) and an automatic voltage regulator (AVR) model. The multiarea system comprises thermal and wind in Area-1, thermal-geothermal in Area-2, and hydrothermal plant in Area-3. Appropriate nonlinearity constraints are provided for both hydro and thermal plants. A maiden attempt has been made to employ a cascade proportional-derivative (PD) with filter coefficient (N) and PID with filter coefficient (N) (CPDN-PIDN) as a secondary controller in the combined ALFC-AVR system and optimized controller gains using artificial flora algorithm (AFA). It is examined that the CPDN-PIDN controller yields better dynamics responses than the PI and PIDN controllers. Analyze reflects that the system performance enhanced in the presence of AVR loop. This paper discusses the effect of renewable sources like wind and geothermal on the system dynamics. Analysis reveals that the geothermal power plant is more reliable than the wind plant. The parameter variations of the temporary and permanent droop and hydro turbine time constant (HTTC) of the hydro plant are examined through system responses. System response deteriorates significantly as permanent droop and HTTC are reduced. Peak overshoot and undershoot are reduced considerably as the temporary droop value is increased. Sensitivity analysis reveals that the AFA optimized CPDN-PIDN controller parameters obtained under the nominal condition are resilient enough and need not be reset with the system's substantial random variation. An experimental justification has been provided of the investigated system using a real-time simulation with the OPAL-RT OP4510 real-time setup.