This paper presents a new voltage control strategy for an electronically-interfaced distribution generation (DG) unit that utilizes a voltage-sourced converter (VSC) as the interface medium. The ...control strategy is based on the concept of voltage-controlled VSC (VC-VSC) rather than the conventional current-controlled VSC (CC-VSC). The proposed VC-VSC 1. enables operation of a DG unit in both grid-connected and islanded (autonomous) modes, 2. provides current-limit capability for the VSC during faults, 3. inherently provides an islanding detection method without non-detection zone, 4. provides smooth transition capability between grid-connected and autonomous modes, and 5. can accommodate ride-through capability requirements under a grid-connected mode. This paper also investigates performance of the proposed VC-VSC strategy based on an eigenanalysis in MATLAB, and time-domain simulations in the PSCAD/EMTDC environment.
Microgrids (MG) are small-scale electric grids with local voltage control and power management systems to facilitate the high penetration and grid integration of renewable energy resources (RES). The ...distributed generation units (DGs), including RESs, are connected to (micro) grids through power electronics-based inverters. Therefore, new paradigms are required for voltage and frequency regulation by inverter-interfaced DGs (IIDGs). Notably, employing effective voltage and frequency regulation methods for establishing power-sharing among parallel inverters in MGs is the most critical issue. This paper provides a comprehensive study, comparison, and classification of control methods including communication-based, decentralized, and construction and compensation control techniques. The development of inverter-dominated MGs has caused limitations in employing classical control techniques due to their defective performance in handling non-linear models of IIDGs. To this end, this article reviews and illustrates advanced controllers that can deal with the challenges that are created due to the uncertain and arbitrary impedance characteristics of IIDGs in dynamics/transients.
Most of the launched power electronics-enabled distributed generators (DGs) adopt phase-locked-loop (PLL) synchronization control. In this paper, we delve into two different autonomous operation ...control (AOC) strategies to ensure the frequency/voltage profile and accurate power sharing for such DGs in islanded systems. The commonly used AOC is based on the concept of active power-frequency (<inline-formula> <tex-math notation="LaTeX">P-f </tex-math></inline-formula>) and reactive power-voltage magnitude (<inline-formula> <tex-math notation="LaTeX">Q-V </tex-math></inline-formula>) droop and deployed in a decentralized way. It is frequently criticized for inaccurate reactive power sharing between DGs, subject to the mismatch in their output impedances. To cope with this issue, we first design a local AOC using the <inline-formula> <tex-math notation="LaTeX">P-f </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">Q-\dot {V} </tex-math></inline-formula> (i.e., the time derivate of <inline-formula> <tex-math notation="LaTeX">V </tex-math></inline-formula>) droop concept, where the desired reactive power sharing can be achieved at the expense of a marginal and allowable <inline-formula> <tex-math notation="LaTeX">V </tex-math></inline-formula> excursion. Then, we develop an optimization-based AOC that is implemented through a continuous-time alternating direction method of multipliers (ADMM) algorithm and neighborhood communication. Equilibrium analysis and local asymptotic stability of the proposed AOC strategies are both established using a Lyapunov method. Finally, simulations are carried out in two islanded systems to validate the improvement in power sharing under a wide range of possible system conditions.
The power start-up operation of a nuclear power plant (NPP) increases the reactor power to the full-power condition for electricity generation. Compared to full-power operation, the power-increase ...operation requires significantly more decision-making and therefore increases the potential for human errors. While previous studies have investigated the use of artificial intelligence (AI) techniques for NPP control, none of them have addressed making the relatively complicated power-increase operation fully autonomous. This study focused on developing an algorithm for converting all the currently manual activities in the NPP power-increase process to autonomous operations. An asynchronous advantage actor-critic, which is a type of deep reinforcement learning method, and a long short-term memory network were applied to the operator tasks for which establishing clear rules or logic was challenging, while a rule-based system was developed for those actions, which could be described by simple logic (such as if-then logic). The proposed autonomous power-increase control algorithm was trained and validated using a compact nuclear simulator (CNS). The simulation results were used to evaluate the algorithm's ability to control the parameters within allowable limits, and the proposed power-increase control algorithm was proven capable of identifying an acceptable operation path for increasing the reactor power from 2% to 100% at a specified rate of power increase. In addition, the pattern of operation that resulted from the autonomous control simulation was found to be identical to that of the established operation strategy. These results demonstrate the potential feasibility of fully autonomous control of the NPP power-increase operation.
Many industries apply traditional controllers to automate manual control. In recent years, artificial intelligence controllers applied with deep-learning techniques have been suggested as advanced ...controllers that can achieve goals from many industrial domains, such as humans. Deep reinforcement learning (DRL) is a powerful method for these controllers to learn how to achieve their specific operational goals. As DRL controllers learn through sampling from a target system, they can overcome the limitations of traditional controllers, such as proportional-integral-derivative (PID) controllers. In nuclear power plants (NPPs), automatic systems can manage components during full-power operation. In contrast, startup and shutdown operations are less automated and are typically performed by operators. This study suggests DRL-based and PID-based controllers for cold shutdown operations, which are a part of startup operations. By comparing the suggested controllers, this study aims to verify that learning-based controllers can overcome the limitations of traditional controllers and achieve operational goals with minimal manipulation. First, to identify the required components, operational goals, and inputs/outputs of operations, this study analyzed the general operating procedures for cold shutdown operations. Then, PID- and DRL-based controllers are designed. The PID-based controller consists of PID controllers that are well-tuned using the Ziegler–Nichols rule. The DRL-based controller with long short-term memory (LSTM) is trained with a soft actor-critic algorithm that can reduce the training time by using distributed prioritized experience replay and distributed learning. The LSTM can process a plant time-series data to generate control signals. Subsequently, the suggested controllers were validated using an NPP simulator during the cold shutdown operation. Finally, this study discusses the operational performance by comparing PID- and DRL-based controllers.
Optimal operation of membrane bioreactor (MBR) plants is crucial to save operational costs while satisfying legal effluent discharge requirements. The aeration process of MBR plants tends to use ...excessive energy for supplying air to micro-organisms. In the present study, a novel optimal aeration system is proposed for dynamic and robust optimization. Accordingly, a deep reinforcement learning (DRL)-based optimal operating system is proposed, so as to meet stringent discharge qualities while maximizing the system's energy efficiency. Additionally, it is compared with the manual system and conventional reinforcement learning (RL)-based systems. A deep Q-network (DQN) algorithm automatically learns how to operate the plant efficiently by finding an optimal trajectory to reduce the aeration energy without degrading the treated water quality. A full-scale MBR plant with the DQN-based autonomous aeration system can decrease the MBR's aeration energy consumption by 34% compared to other aeration systems while maintaining the treatment efficiency within effluent discharge limits.
Herbal remedies are in most cases still manufactured with traditional equipment installations and processes. Innovative chemical process engineering methods such as modeling and process ...intensification with green technology could contribute to the economic and ecologic future of those botanicals. The integration of modern unit operations such as water-based pressurized hot water extraction and inline measurement devices for process analytical technology approaches in traditional extraction processes is exemplified. The regulatory concept is based on the quality-by-design demand for autonomous feed-based recipe operation with the aid of digital twins within advanced process control. This may include real-time release testing to the automatic cleaning of validation issues. Digitalization and Industry 4.0 methods, including machine learning and artificial intelligence, are capable of keeping natural product extraction manufacturing and can contribute significantly to the future of human health.
To realize underwater accurate vision-based operations, this article proposes a hybrid visual servoing control scheme for underwater vehicle manipulator systems (UVMSs) toward a moving target. First, ...the position-based visual servoing (PBVS) scheme is purposed based on the binocular camera. Second, to reduce the influence of hand-eye system uncertainties, an uncalibrated visual servoing (UVS) control scheme based on the monocular camera is presented. The partitioned Broyden's method is utilized to estimate the Jacobian matrix, and a modified adaptive Broyden's class method is proposed to obtain the approximation of the residual matrix. Afterward, in order to realize long range accurate operations, a novel hybrid visual servoing scheme with Jacobian matrix fusion algorithm is presented not only to combine the PBVS scheme and the UVS scheme but also to avoid the trial movements' influence. Simulations and an experiment are conducted to testify the robustness and effectiveness of the presented scheme.
This paper presents a new control strategy for islanded (autonomous) operation of an electronically coupled distributed generation (DG) unit and its local load. The DG unit utilizes a voltage-sourced ...converter (VSC) as the coupling medium. In a grid-connected mode, based on the conventional dq-current control strategy, the VSC controls real- and reactive-power components of the DG unit. Subsequent to an islanding detection and confirmation, the dq-current controller is disabled and the proposed controller is activated. The proposed controller utilizes (1) an internal oscillator for frequency control and (2) a voltage feedback signal to regulate the island voltage. Despite uncertainty of load parameters, the proposed controller guarantees robust stability and prespecified performance criteria (e.g., fast transient response and zero steady-state error). The performance of the proposed controller, based on time-domain simulation studies in the PSCAD/EMTDC software environment, is also presented.
To minimise the number of load sheddings in a microgrid (MG) during autonomous operation, islanded neighbour MGs can be interconnected if they are on a self-healing network and an extra generation ...capacity is available in the distributed energy resources (DER) of one of the MGs. In this way, the total load in the system of interconnected MGs can be shared by all the DERs within those MGs. However, for this purpose, carefully designed self-healing and supply restoration control algorithm, protection systems and communication infrastructure are required at the network and MG levels. In this study, first, a hierarchical control structure is discussed for interconnecting the neighbour autonomous MGs where the introduced primary control level is the main focus of this study. Through the developed primary control level, this study demonstrates how the parallel DERs in the system of multiple interconnected autonomous MGs can properly share the load of the system. This controller is designed such that the converter-interfaced DERs operate in a voltage-controlled mode following a decentralised power sharing algorithm based on droop control. DER converters are controlled based on a per-phase technique instead of a conventional direct-quadratic transformation technique. In addition, linear quadratic regulator-based state feedback controllers, which are more stable than conventional proportional integrator controllers, are utilised to prevent instability and weak dynamic performances of the DERs when autonomous MGs are interconnected. The efficacy of the primary control level of the DERs in the system of multiple interconnected autonomous MGs is validated through the PSCAD/EMTDC simulations considering detailed dynamic models of DERs and converters.
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