Tertiary Control Research Articles

Expression of microRNA-21 in acute ischemic stroke: relationship with inflammatory cytokines, clinical severity, and clinical outcome

Background When the brain is deprived of oxygen and nutrients due to stenosis or arterial rupture, neurons in the affected area suffer irreversible damage and cellular death. MicroRNA has been shown to regulate target genes implicated in arterial hypertension, atherosclerosis, and diabetes mellitus, all of which influence the risk of ischemic stroke through inflammation, oxidative stress, cell proliferation, and apoptosis. The study aims to determine the changes in miRNA expression, namely miRNA-21, between acute ischemic stroke patients and controls and their relationship to proinflammatory cytokines, clinical severity, and outcome. Methods Serum samples from tertiary hospitals and controls were used to evaluate miRNA-21 expression as well as cytokines TNF-α, IL-10, ICAM-1, and CCL5 levels within 7 days of stroke onset. The 30-day clinical severity and outcome were assessed using the National Institute of Health Stroke Scale (NIHSS) and modified Rankin Scale (mRS), respectively. Result A total of 64 acute ischemic stroke patients and 22 age-matched controls were recruited, with median ages of 56 and 55.5 years old, respectively. There were more male subjects than females (35 to 29). A statistically significant difference was observed in miRNA-21 expression between patients and controls (p<0.001). This finding implies that miRNA-21 expression may have a contribution in acute stroke patients. This was followed by an increase in proinflammatory markers TNF-α, IL-10, ICAM-1, and CCL5. However, no association was found between miRNA-21 and any pro-inflammatory cytokine. There was no significant correlation between miRNA-21 or cytokines markers with clinical severity or prognosis. Conclusion Our study demonstrated increased miRNA-21 expression and proinflammatory cytokine expression in acute ischemic stroke patients relative to controls. However, this was not related to clinical severity or clinical outcomes.

A new representation learning based maximum power operation towards improved energy management integration with DG controllers for photovoltaic generators using online deep exponentially expanded RVFLN algorithm

To incorporate Local Energy Management System (LEMS) based Tertiary Controller (TC) operation with multiple Photovoltaic based Distributed Generations (PV-DGs) level Primary Controller/ PC: Independent DG Controllers (IDGC) more adequately, a new representation learning (Symmetrical Input-Output weight Encoded Data Compression/ SIODC, with Exponential Expansion based Random Vector Functional Link Network towards Softmax layer’s Control Reference Estimation/ ExRVFLN-SoCRE) based MPPT controller is proposed in this paper in terms of Secondary Controllers (SCs). A new Hybrid LEMS (HyLEMS) is presented towards the proposed Deep Neural Network based SCs (i.e. DNN-SCs) in a distributed (SCdist), as well as centralized (SCcent) manner, to ease the computational, and communication requirements. To investigate that multiple PV-DGs based duty cycle (kth instant Control References/CRs estimation) controllers are considered here with auxiliary Battery Energy Storage Systems (BESS), and AC utility, integrated to Common DC feeder in terms of DC-DC converter dynamics. To avoid Control Reference Estimation Error (CREE) due to initial randomization, optimization subroutines are incorporated for SIODC by generalized semi-supervised learning, and for ExRVFLN-SoCRE by Lagrange multiplier weighted, rms based cost function. The proposed control performance is verified in MATLAB Simulink® based average modeling, and validated through dSPACE DS1104 based RTI with multi-PV (emulators) test-bench as well.

Short-term high-efficiency decision-making method for tertiary voltage control considering short-term power fluctuations

Abstract With the continuous increase in grid-connected new energy sources and new types of intermittent loads, the short-term fluctuation amplitude of source-load power is continuously increasing, which increasingly affects the effectiveness of tertiary voltage control decisions. To address this issue, this paper proposes a short-term efficient decision-making method for tertiary voltage control considering short-term power fluctuations. Firstly, the decision-making cycle of tertiary voltage control is shortened, effectively enhancing its adaptability to short-term source-load power fluctuations. Secondly, to address the computational burden brought by the shortened decision cycle, this paper fully considers the temporal characteristics of short-term load fluctuations in practical power grid nodes and proposes two linearization methods for tertiary voltage control. Specifically, these methods include a three-point linear estimation method for small fluctuation periods and a sensitivity linearization optimization method for large fluctuation periods. By shortening the decision cycle and linearizing decision calculations, the efficiency of tertiary voltage control decision-making is greatly improved. Based on actual data from a provincial-level power grid in the northwest region of China, the effectiveness of the proposed methods is verified through simulation analysis.

Topology-aware reinforcement learning for tertiary voltage control

Transmission systems have experienced an increase in the occurrence frequency and intensity of high voltage events over the past few years. Since traditional approaches to optimal power flow do not scale well to real-life systems, it has become urgent to develop new methods to help operators improve tertiary voltage control. In this paper, we propose to train a graph neural network to choose voltage setpoints by interacting with a power grid simulator using reinforcement learning techniques. Moreover, we introduce the hyper heterogeneous multi graph formalism to account for topology variations of real-life systems (assets disconnection, bus-splitting, etc.). Our approach is validated on an artificial case study based on the case60nordic power grid.

Cyber–physical collaborative control for DC microgrid clusters under joint cyber-attacks

DC microgrid (DCMG) clusters, as deeply integrated cyber–physical systems (CPSs), are vulnerable to cyber-attacks like false data-injection attacks (FDIAs) and denial-of-service (DoS) attacks. This article proposes a cyber–physical collaborative control method, against the joint attacks mentioned above. The main contribution of this work is as follows: firstly, a collaborative control architecture based on multiagent systems (MASs) is constructed for the CPS; secondly, in the physical layer, a hierarchical control framework for DCMG agents is adopted, including primary, secondary, and tertiary control layers. The secondary and tertiary control layers can adjust the scheme of power flow management and frequency/voltage control, through the interaction information between agents; thirdly, in the cyber layer, an active defense strategy based on layered event-triggered protocol has been proposed: (1) according to the tampering of transmission data/information caused by FDIA, a support vector machine (SVM) based prediction learning compensation method is employed, to ensure the effectiveness of energy management and voltage/frequency control. (2) according to the transmission path interruption caused by DoS attack, the optimal path selection method and path reconstruction method are proposed to maintain the transmission of power flow management information and control information. Simulation results are presented to illustrate the effectiveness of real-time collaborative control for the proposed control method.

Secondary Control Strategies in the DC Microgrids

Now, DC microgrids have become more popular for several reasons, including the lack of issues related to reactive power and frequency control, the direct integration of energy storage devices and solar photovoltaic, and the higher utilization of DC loads. A DC microgrid using several sources (distributed generation) is a popular research area. The main issue in such a DC microgrid is to provide good voltage regulation and proportional power sharing among all sources. Control strategy is very important to solve the above issue in order to maintain the reliability and stability of DC microgrids. Hence, a comprehensive review of DC microgrid control techniques is essential. Compared to other methods, hierarchical control is widely used to solve the aforementioned issue. It consists of three layers of control: primary control, secondary control, and tertiary control. At the primary level, to improve current sharing performance, droop control is usually applied. Secondary control is used for voltage regulation of the DC bus. A tertiary control is a higher-level control to achieve Optimization and economical grid operation. In traditional primary control, it is not possible to attain accurate power sharing and voltage regulation simultaneously. Thus, secondary control is required. So, this paper reviews the secondary level control techniques in the hierarchical control strategy for DC microgrids. Precisely, Centralized, distributed, and decentralized approach-based secondary control are reviewed. Several secondary control techniques have been thoroughly examined in terms of their advantages and disadvantages making this an excellent resource for both academics and business executives.

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Hierarchical-power-flow-based energy management for alternative/direct current hybrid microgrids

Modern microgrids are systems comprising both Alternative Current (AC) and Direct Current (DC) subgrids, integrated with Distributed Generations (DGs), storage systems, and Electric Vehicles (EVs) parking facilities. Achieving stable and reliable load flow control amidst varying load, generation, and charging/discharging strategies requires a hierarchical control scheme. This paper proposes an hourly power flow (PF) analysis within an Energy Management System (EMS) for AC/DC Hybrid Microgrids interconnected via an Interlinking Converter (IC) in both grid-connected and islanded modes. The framework operates within a two-level hierarchically controlled platform. Tertiary control at the top level optimizes DGs' reference power for generation and consumption, minimizing power purchase costs and load shedding in grid-connected and islanded modes, respectively. DG converters employ current control mode to share their power references as the primary controller. While no secondary controller is adopted in this scheme, the Battery Energy Storage System (BESS) in islanded mode utilizes P/Q droop control to maintain voltage and frequency in the AC subsystem. Power sharing between AC and DC subgrids through IC is determined by the difference between AC grid frequency and DC link voltage. Integration of controlled converters’ buses into PF equations enables solving the unified system using the traditional Newton-Raphson (NR) method. A segment of a real distribution grid planned for installation in Italy under the HYPERRIDE project serves as a case study. Comparison with MATLAB/Simulink results confirms the effectiveness, precision, and convergence speed of the proposed model and control schemes, demonstrating efficient load distribution and voltage/frequency restoration in islanded mode.

The design of an improved index system for frequency control in China Southern Power Grid

Introduction: In order to dispatch frequency regulation resources in regional power grids efficiently and promote the development of spot markets, China Southern Power Grid (CSG) established the unified frequency regulation control area. However, the existing regional control performance standards (CPS) for evaluating the performance of frequency control in bulk power systems is no longer suitable for the unified frequency control mode.Method: This paper proposed an innovative frequency control performance standard, named tertiary control performance standards (TCPS) and used Gaussian mixture model (GMM) and folded normal distribution (FND) to describe the distributions of frequency deviations and power deviations of tie line.Result and Discussion: Based on these probability models, the parameters of the proposed TCPS can be determined optimized. Finally, case studies were carried out with the practical data from CSG and indicated that the parameters of proposed TCPS index could be calculated and improved the control performance for the real time power balance of the regional power grid with spot markets.

Enhancing voltage profile in islanded microgrids through hierarchical control strategies

This paper presents a novel approach aimed at enhancing the voltage profile of an islanded microgrid (MG) through the utilization of Phasor Measurement Units (PMUs) and a hierarchical control system for Distributed Generators (DGs). Recognizing the criticality of maintaining an optimal voltage profile for essential loads within an MG, we assume that PMUs are strategically placed across the MG based on existing placement methodologies to ensure the observability of load buses. The synchrophasor measurements captured by these PMUs are leveraged within an offline-designed tertiary control layer of the hierarchical framework, which in turn sets the voltage reference for the secondary control layer of DGs across the MG. This approach effectively enhances the voltage profile of vital load buses within the MG. The proposed hierarchical strategy encompasses primary, secondary, and tertiary levels of control, responsible for stabilizing voltage and frequency, computing power references, and managing power flow, respectively. PMU measurements play a crucial role in the secondary control level by continuously monitoring deviations. By ensuring the effective placement of PMUs, the proposed framework enables efficient control, leading to notable improvements in the voltage profile and power management. To evaluate the effectiveness of our approach, five test networks were used: 14-bus, 30-bus, 39-bus, 57-bus, and 118-bus test networks. Simulation results demonstrate an impressive over 80 % enhancement in the MG's voltage profile achieved by our proposed method. Additionally, our heuristic strategy efficiently assigns the closest load bus to each DG while aggregating equidistant bus deviations. Collectively, our research underscores the significant impact of PMU measurements and a well-structured hierarchical voltage control system in substantially improving the voltage profile of MGs.

Redispatch Model for Real-Time Operation with High Solar-Wind Penetration and Its Adaptation to the Ancillary Services Market

Modern electrical power systems integrate renewable generation, with solar generation being one of the pioneers worldwide. In Latin America, the greatest potential and development of solar generation is found in Chile through the National Electric System. However, its energy matrix faces a crisis of drought and reduction of emissions that limits hydroelectric generation and involves the definitive withdrawal of coal generation. The dispatch of these plants is carried out by the system operator, who uses a simplified mechanism, called “economic merit list” and which does not reflect the real costs of the plants to the damage of the operating and marginal cost of the system. This inefficient dispatch scheme fails to optimize the availability of stored gas and its use over time. Therefore, a real-time redispatch model is proposed that minimizes the operation cost function of the power plants, integrating the variable generation cost as a polynomial function of the net specific fuel consumption, adding gas volume stock restrictions and water reservoirs. In addition, the redispatch model uses an innovative “maximum dispatch power” restriction, which depends on the demand associated with the automatic load disconnection scheme due to low frequency. Finally, by testing real simulation cases, the redispatch model manages to optimize the operation and dispatch costs of power plants, allowing the technical barriers of the market to be broken down with the aim of integrating ancillary services in the short term, using the power reserves in primary (PFC), secondary (SCF), and tertiary (TCF) frequency control.

Clinical decision support system based on deep learning for evaluating implantable collamer lens size and vault after implantable collamer lens surgery: a retrospective study

ObjectivesTo aid doctors in selecting the optimal preoperative implantable collamer lens (ICL) size and to enhance the safety and surgical outcomes of ICL procedures, a clinical decision support system (CDSS)...

A Modified Particle Swarm Algorithm for the Multi-Objective Optimization of Wind/Photovoltaic/Diesel/Storage Microgrids

Microgrids have been widely used due to their advantages, such as flexibility and cleanliness. This study adopts the hierarchical control method for microgrids containing multiple energy sources, i.e., photovoltaic (PV), wind, diesel, and storage, and carries out multi-objective optimization in the tertiary control, i.e., optimizing the economic cost, environmental cost, and the degree of energy utilization of microgrids. As the traditional multi-objective particle swarm algorithm is prone to local convergence, this study introduces variable inertia weight and learning factors to obtain a modified particle swarm algorithm, which is more advantageous in multi-objective optimization. Compared to the traditional particle swarm algorithm, the modified particle swarm algorithm increased the photovoltaic absorbed rate from 0.7724 to 0.8683 and the wind energy absorbed rate from 0.6064 to 0.7158 in one day, which resulted in an increase in energy utilization by 14.89%, and a reduction in financial environmental costs from RMB 135,870 to RMB 132,230. The simulation of the optimization effect of the conventional particle swarm algorithm and the modified particle swarm algorithm on the microgrid were carried out, respectively, in MATLAB, which verifies the advantage of the modified particle swarm algorithm on the optimization of microgrids. Then, the optimization results, i.e., the data of the power scheduling process of the four power sources, were made into a table and imported into the microgrid model in Simulink. The simulation results indicated that the microgrid was able to output stable voltage, current, and frequency. Finally, the changes in microgrids affected by the external environment were further investigated from the aspects of the market environment and natural environment. Moreover, we verified the presence of a contradiction between the optimization of the microgrid economy and environmental protection. Thus, microgrids need to adjust their optimization focus according to the natural conditions in which they are located.

Advanced Concept for Participation of Large Wind Farms in Ancillary Services

The use of renewable energies is steadily growing. Wind energy is the main installed type of renewable energy in Germany. Wind power plants are concentrated in large wind farms. These wind farms are connected to high voltage grids. Despite the high level of installed wind power, wind farms have not contributed to voltage and frequency control (primary, secondary and tertiary control) until now. Wind farm owners receive a fixed payment from the transmission system operator for the energy they generate. This payment is much higher than the average variable costs of conventional power plants. This study shows that modern wind power plants and farms can also support voltage and frequency control. Wind farm owners receive more income through participating in frequency control than from the fixed payment for energy generated. This extra income can help to reduce the fixed payment for energy generated by wind farms.

A Variable Tertiary DC-Link Voltage Control for integrated APM in EV Charger Systems Featuring Full ZVS Range

A Variable Tertiary DC-Link Voltage Control for integrated APM in EV Charger Systems Featuring Full ZVS Range

A novel snow conditions‐compatible computational intelligence‐based PV power forecasting approach for microgrids in snow prone regions

AbstractEnergy management in a renewable energy‐based microgrid has a key role in improving energy utilisation and reducing the microgrid operation cost. The optimal energy management strategy can be significantly affected by the intermittency of renewable energies and also harsh weather conditions. In this study, a novel snow conditions‐compatible computational intelligence‐based short‐term photovoltaic (PV) power forecasting (PVPF) approach is proposed that is independent of exogenous weather forecasts. The proposed approach consists of a snow cover detection stage, a snow cover forecasting stage, and a PV power forecasting stage. This approach is then validated for a model predictive control (MPC)‐based energy management system (EMS) of a PV energy‐based grid‐connected microgrid located in a snow‐prone area. The PVPF method together with a computational intelligence‐based short‐term load demand forecasting model constitutes the forecasting block of the EMS. The forecasting block generates day‐ahead hourly forecasts based on the local measurements of the meteorological‐electrical parameters and sends them to the optimisation block where a two‐stage control method, corresponding to the tertiary and secondary control levels, is developed based on mixed‐integer linear and quadratic programming. The developed EMS is applied to a test microgrid simulated in MATLAB/Simulink and compared with a heuristic control method. The results show that the proposed approach can reduce the overall operation cost of the microgrid by 8% (24$), 15% (166$), and 13% (235$) on sunny, cloudy, and snowy days under study, respectively, compared to the heuristic controller.

A new real-time maximum power point tracking scheme for PV-BASED microgrid STABILITY using online DEEP ridge extreme learning machine algorithm

A new deep representation-based Maximum Power Point Tracking (MPPT) controller is proposed in this paper for accurate Control references calculation in Photovoltaic (PV) based Micro Grid (MG) operation. The deep representation is obtained by two-step estimation: Data dimension reduction and MPPT Tracker towards optimal computation. The considered deep learning architecture is targeted for N number (large scale) of PV-based DGs, connected locally in the distribution system (DC link extended to AC utility). The collected data of solar irradiation (in W/m2) and PV panel temperature (in oC) profiles of local DGs are subjected to data dimensions using Extreme Learning Machine (ELM) based on Moore-Penrose inverse technique. The compressed represented PV-DG data is further communicated to the Tertiary Control side MPPT Tracker, where Ridge Regression-based ELM is presented for estimating Maximum Power Point Power (PMPP) and Voltage (VMPP) values for kth instant. The initial randomness present in the proposed Deep Representation based Ridge Regression Extreme Learning Machine (DR-RRELM) is further minimized by adopting Huber's characteristic distribution-based likelihood estimator. The proposed MPPT scheme is effectively implemented for accurate control reference in DC-DC and DC-AC converters in the MG. The proposed controller is also suitable for stability improvement at point of common coupling (PCC). Three different case studies such as past data verification, stability analysis under various operating conditions, irradiant change and source power variation. The efficacy of the proposed deep representation-based MPPT scheme is evidenced in MATLAB-based simulation. The proposed technique provides better tracking ability, faster learning and effective reference generation. The case study with irradiation change is validated in TMS320C6713 (32-bit) based Hardware-in-Loop (HIL) validation.

Cost‐effective secondary voltage control in dc shipboard integrated power system

AbstractFast and dramatic voltage disturbances caused by the electric propulsion and dynamic positioning process are severe issues in the DC shipboard integrated power system (DC‐SIPS). The secondary voltage control (SVC) equipped in the diesel genset (DG) and hybrid energy storage system (HESS) becomes an effective solution. However, the conventional SVC cannot distinguish the different voltage regulation characteristics between DG and HESS in a cost‐effective way when allocating the voltage regulation responsibility (VRR). The cost optimization achieved by the conventional tertiary control cannot be used for the DC‐SIPS due to frequent and unpredictable load fluctuations. In this paper, a cost‐effective secondary voltage control is proposed to realize voltage restoration and cost minimization simultaneously. First, the voltage regulation cost model is developed considering the different voltage regulation characteristics of DG and HESS. Then, the optimization problem of VRR distribution is addressed by minimizing the total voltage regulation cost function using a quadratic programming algorithm. Moreover, the voltage regulation ability of each energy storage system is fully used with the state of charge balance. Thus, the conflict among voltage restoration, cost minimization, and SoC balance is effectively addressed. Finally, promising hardware‐in‐loop test results illustrate the effectiveness of the proposed method.

Development of Control Techniques for AC Microgrids: A Critical Assessment

This article aims to provide a comprehensive review of control strategies for AC microgrids (MG) and presents a confidently designed hierarchical control approach divided into different levels. These levels are specifically designed to perform functions based on the MG’s mode of operation, such as grid-connected or islanded mode. The primary control ensures frequency (f) and voltage (V) stability, whereas the secondary control adjusts their values to their references and the tertiary control efficiently manages the power of distributed generators (DGs) in a cost-effective manner. The article extensively discusses centralized, decentralized, and distributed strategies for each control level, highlighting their differences, advantages, disadvantages, and areas of application. Finally, the usefulness of different control strategies at different levels is demonstrated through practical examples.

Blockchain-enabled peer-to-peer energy trading and resilient control of microgrids

The deregulation and decentralization of the energy market have resulted in a proliferation of distributed generation that participates in energy trading as prosumers. In peer-to-peer (P2P) trading of energy within the microgrid (MG), the peers can trade energy without the need for an intermediary. Blockchain technology is devised to assure the security and resilience of the system's P2P trading against adversarial attacks. The large number of renewable prosumers who participate in trading raises the MG system's oscillation frequency. To regulate the system frequency during trading, a distributed-based federated learned fractional-order recurrent neural network (FL-FORNN) adaptive controller is proposed. The control system is a crucial component of MGs in order to ensure stable performance. To aggregate the network weights, the proposed FL-based controller frequently communicates with the cloud server. To avoid the privacy threat during this case, we further propose to integrate FL with local differential privacy (LDP) to secure against the false data injection attack from the eavesdropper. To validate, the MG model is implemented in OPAL-RT with its resilient controller. The P2P trading of energy in the blockchain is executed in Raspberry Pis (RPis) based on the numerous prosumers/consumers participating in the trading. Then, the power information from the tertiary control implemented in RPis is communicated with the MG secondary frequency controller by interfacing using the user datagram protocol. The proposed work is realized for the MG considering four prosumers and three consumers, and the resiliency of the controller is authenticated with case studies. The results divulge that the LDP of the proposed controller can provide a robust and secure solution of MGs with P2P trading, even in the presence of adversarial attacks.