Generation Control Research Articles

Ultrafast Permittivity Engineering Enables Broadband Enhancement and Spatial Emission Control of Harmonic Generation in ZnO

Ultrafast Permittivity Engineering Enables Broadband Enhancement and Spatial Emission Control of Harmonic Generation in ZnO

Optimal Feature Selection and Classification for Parkinson’s Disease Using Deep Learning and Dynamic Bag of Features Optimization

Parkinson’s Disease (PD) is a neurological condition that worsens with time and is characterized bysymptoms such as cognitive impairment andbradykinesia, stiffness, and tremors. Parkinson’s is attributed to the interference of brain cells responsible for dopamine production, a substance regulating communication between brain cells. The brain cells involved in dopamine generation handle adaptation and control, and smooth movement. Convolutional Neural Networks are used to extract distinctive visual characteristics from numerous graphomotor sample representations generated by both PD and control participants. The proposed method presents an optimal feature selection technique based on Deep Learning (DL) and the Dynamic Bag of Features Optimization Technique (DBOFOT). Our method combines neural network-based feature extraction with a strong optimization technique to dynamically choose the most relevant characteristics from biological data. Advanced DL architectures are then used to classify the chosen features, guaranteeing excellent computational efficiency and accuracy. The framework’s adaptability to different datasets further highlights its versatility and potential for further medical applications. With a high accuracy of 0.93, the model accurately identifies 93% of the cases that are categorized as Parkinson’s. Additionally, it has a recall of 0.89, which means that 89% of real Parkinson’s patients are accurately identified. While the recall for Class 0 (Healthy) is 0.75, meaning that 75% of the real healthy cases are properly categorized, the precision decreases to 0.64 for this class, indicating a larger false positive rate.

Photonic hook shaping achieved by the micro-nano fiber array based Janus cylindrical metalens

Abstract Near-field focusing of an electromagnetic wave on the assembly of hexagonally asymmetric arranged close-contact nanofibers into a cylindrical Janus metalens is considered for different fiber sizes and optical properties. We propose combining the meta-material concept with a photonic hook based on the finite-difference time-domain technique. Simulation results show that the cylindrical meta-photonic Janus structure produces the focal region of enhanced optical intensity, which exists in the spatial form of a localized structured light known as a photonic hook. A detailed study of all key parameters of a photonic hook depending on the Janus metalens topology and optical properties of the fiber filling is carried out. The structure conditions have been determined for the beam waist of a photonic hook that is less than the diffraction limit. This Janus cylindrical metalens may contribute to the versatile control of photonic hook generation in the applications of bio-photonics and optical microscopes.

Corrigendum: The application of virtual synchronous generator technology in inertial control of new energy vehicle power generation

Corrigendum: The application of virtual synchronous generator technology in inertial control of new energy vehicle power generation

Advanced Sliding Mode Design for Optimal Automatic Generation Control in Multi-Area Multi-Source Power System Considering HVDC Link

The multi-area multi-source power system (MAMSPS), which uses a variety of power sources including gas, hydro, thermal, and renewable energy, has recently been implemented to balance the growing demand for electricity and the overall capacity for power generation. In this paper, an integral sliding mode control with a single-phase technique (ISMCSP) is applied to two areas, with each area including gas–wind–thermal power systems with HVDC system. Firstly, a two-area gas–wind–thermal power system with HVDC (TAGWTPSH) is the first model in this scheme to consider the parameter uncertainties of a MAMSPS. Secondly, sliding mode design law with a single-phase technique is introduced to alleviate chattering and oscillation problems. Then, power system stability is ensured by the Lyapunov control theory based on the new LMIs technique. Thirdly, the ISMCSP’s effectiveness in a MAMSPS is also assessed under random load patterns and parameter variations regarding settling time and over-/undershoot. The ISMCSP was created to alter the fundamental sliding mode control, and therefore the suggested approach performs better than recently published approaches. This is demonstrated by the frequency overshoot deviation value in frequency deviations: 0.7 × 10−3 to 2.8 × 10−3 for the TAGWTPSH with the suggested ISMCSP. In the last case, for random changes in load from −0.4 to +0.5 p. u, the proposed ISMCSP method still stabilizes the frequency of the areas meeting the standard requirements for AGC.

System modeling-based investigation of SMR operations using variable T-avg control for cost effective electricity generation in a grid

System modeling-based investigation of SMR operations using variable T-avg control for cost effective electricity generation in a grid

Flexible heat and power load control of subcritical heating units based on energy demand-supply balance

To fulfill the requirement for enhancing the flexibility of heating units within the framework of renewable energy grid integration. In this study, a proposed optimal control strategy for heating units borrows the heat extraction output to perform rapid load changes; the boiler heat supply within the unit is clearly differentiated from the turbine energy demand, and formulate corresponding control signals. The coordinated control of the boiler and turbine based on the energy demand-supply balance of the unit as secondary control for the final load accuracy, ensuring its safe and stable operation. To further enhance the control performance, an improved sparrow optimization algorithm with butterfly search is designed to adjust the controller parameters. Finally, field tests on a 300MW subcritical heating unit reveal that the automatic generation control performance index is improved by 32.16% compared to the traditional coordinated control strategy, while taking less impact on the heat user. These results validate the exceptional performance and significant practical value of the optimized control strategy.

Multi-objective design of fractional frequency-load control for hydro-thermal system considering nonlinear models and uncertainty

One of the crucial quantities in the power system problem is frequency, which should never exceed its nominal value. In normal conditions of load changes, the frequency is adjusted by automatic generation control. Still, in the face of special conditions, such as logging of the downstream network from the main network or a significant amount of generated power leaving the circuit, the power imbalance between generation and consumption causes a rapid frequency drop. In this case, the automatic generation control cannot stop the frequency drop, so the frequency tends toward instability. In this paper, to automatically produce a water-thermal system in two zones of discrete and continuous modes, the design of a fractional controller is discussed and investigated in different functional conditions and uncertainties. The proposed NSGA-II multi-objective method is used in this article to optimize each area’s load frequency control parameters for various goals. The ideal response for the entire network will be obtained by modifying these responses using a multi-factor method. This paper defines a multi-zone frequency load control based on time objective functions and eigenvalues. Based on the speed changes for the proposed algorithm and the novel approachfunction method employed in the control design. The simulation performed and analyzed in different working conditions with different comparative criteria has been discussed and investigated. Compared to more contemporary techniques, the simulation results show that the system’s performance is guaranteed. The main successes of the suggested method in comparison to earlier methods have been the reduction of overshoot, undershoot, and settling time.

Attack-Dependent Adaptive Event-Triggered Security Fuzzy Control for Nonlinear Networked Cascade Control Systems Under Deception Attacks

This article investigates the issue of H∞ security output feedback control for a nonlinear networked cascade control system with deception attacks. First, to further reduce the amount of communication data, reasonably schedule network resources, and alleviate the impact of multi-channel deception attacks, an attack-dependent adaptive event-triggered mechanism is introduced into the primary network channel, and its adaptive triggered threshold can be adjusted according to the random attack probability. Secondly, the output dynamic quantization of the secondary network channel is considered. Then, a novel security cascade output feedback controller design framework based on the Takagi–Sugeno (T-S) fuzzy networked cascade control system under deception attacks is established. In addition, by introducing the Lyapunov–Krasovskii stability theory, the design conditions of the controller are given. Finally, the effectiveness and superiority of the proposed design strategies are verified by two simulation examples of power plant boiler–turbine system and power plant boiler power generation control system.

Back to the nest: the practices employed for succession of heirs in family businesses in Brazil

PurposeThe present study aims to identify the practices employed to bring heirs into family businesses as successors.Design/methodology/approachWe conducted an exploratory, qualitative investigation using a case study approach. Semi-structured face-to-face interviews were conducted with external consultants and with incumbent leaders, next-generation heirs working in the firm (and likely to become successors) and employees from three family firms from different industries and under ownership and control of different generations of their respective families (first, second and third and fourth generations). In addition to surveying their general perceptions of the succession processes in their firms, each informant was asked to rate the degree of importance of 12 succession practices identified in the literature and the extent to which they exist in their respective firms.FindingsOur results showed that heirs typically enter the family business after a development process outside of the family business, which we have termed as coming back to the nest. This process was enacted through practices that we allocated to the following categories: continued development of heirs, developing relationships in the succession process, separation of roles and attitude of the successor heirs. Overall, 8 of the 12 practices derived from the theoretical framework were endorsed as important by representatives of the family businesses and 9 were endorsed by the consultants, 7 of which coincided in both groups. However, only 5 of the practices were identified as present in the firms’ succession processes by the representatives of the family businesses, while the consultants did not identify any of the 12 practices as present.Originality/valueWe present additional important practices, the adoption of which would be beneficial for family business succession, such as adapting external learning to the family business, acquiring leadership skills and experience and developing emotional intelligence. Our study advances the prior literature since we do not merely discuss succession planning but analyze in an applied manner how succession actually takes place in family businesses.

A mMSA-FOFPID controller for AGC of multi-area power system with multi-type generations

The exceptional growth in the penetration of renewable sources as well as complex and variable operating conditions of load demand in power system may jeopardize its operation without an appropriate automatic generation control (AGC) methodology. Hence, an intelligent resilient fractional order fuzzy PID (FOFPID) controlled AGC system is presented in this study. The parameters of controller are tuned utilizing a modified moth swarm algorithm (mMSA) inspired by the movement of moth towards moon light. At first, the effectiveness of the controller is verified on a nonlinear 5-area thermal power system. The simulation outcomes bring out that the suggested controller provides the best performance over the lately published strategies. In the subsequent step, the methodology is extended to a 5-area system having 10-units of power generations, namely thermal, hydro, wind, diesel, gas turbine with 2-units in each area. It is observed that mMSA based FOFPID is more effective related to other approaches. In order to establish the robustness of the controller, a sensitivity examination is executed. Then, experiments are conducted on OPAL-RT based real-time simulation to confirm the feasibility of the method. Finally, mMSA based FOFPID controller is observed superior than the recently published approaches for standard 2-area thermal and IEEE 10 generator 39 bus systems.

A new integrated modelling and control of ternary pumped storage hydro power generation for small signal stability studies

Pumped storage hydro power generation (PSHPG), a reliable renewable energy source with an energy storage feature, can impart efficient damping torque to power system oscillations to improve small signal stability with appropriate modelling and additional compensation through the governor. But ternary PSHPG (T-PSHPG) has the unique feature of hydraulic short circuit (HSC) mode, where both pumping and generating modes operate simultaneously. This study presents a novel fourth-order modified Heffron Phillips (MHP) model of T-PSHPG in coordination with a multi-band Power system stabilizer (MB-PSS) employing 2-way penstock, where primary penstock circulates water from the upper reservoir to the lower reservoir through the turbine and secondary penstock divides primary penstock, thereby creating short-circuit path through the pump. Also, this model includes additional reactive power droop control to maintain the voltage profile. A new state space matrix has been developed for coordinating T-PSHPG in HSC mode with MB-PSS. The control parameters of the proposed coordinated model are set by a new multi-objective differential evolution-improved particle swam optimization (DE-IPSO) algorithm. With variable load, random solar and wind source penetrations, it has been found that the proposed model can provide adequate damping actions. The sensitivity of the proposed model has been observed by varying critical model parameters by ±50 %. For all conditions, the settling time of speed variation is within 2.672 s to 5.951 s and the minimum damping ratio lies within 0.105 to 0.455. The minimum damping ratio is 0.32 and the settling time is found to be 3.1 s for system response subject to sudden solar power variation, which has been observed by shifting system eigenvalues toward the left half of the complex plane. Time and frequency domain parameters with sensitivity analysis predict system oscillations being damped effectively with consistency for the proposed modelling and control strategy. The results are verified in real-time with OPAL-RT real-time digital simulator. It has been justified by the present study that appropriate modelling of T-PSHPG along with coordinated control action provided by MB-PSS can handle critical power system oscillations efficiently and effective damping torque can be imparted in HSC mode.

Decomposition prediction fractional-order active disturbance rejection control deep Q network for generation control of integrated energy systems

The number of distributed renewable energy sources of integrated energy systems (IESs) are increasing. The volatility of renewable energy exacerbates active power imbalances and increase frequency deviations in power systems. This work proposes a decomposition prediction fractional-order active disturbance rejection control deep Q network (DPFOADRCDQN) to control each generator of IESs more accurately and to reduce systemic frequency fluctuation. According to the fluctuation size of frequency deviation, the sub-signal obtained after the prediction of the modal decomposition of frequency deviation is classified into big fluctuating signals and small fluctuating signals; fractional-order active disturbance rejection control (FOADRC) controls small fluctuating signals; deep Q network (DQN) controls big fluctuating signals. Frequency deviation signals in complex power systems with a large percentage of renewable energy have higher smoothness after modal decomposition, FOADRC can efficiently follow the small fluctuating signals, and DQN can learn the rich data features in the big fluctuating signals and give precise control commands. The DPFOADRCDQN, proportional-integral-derivative, Q-learning, fuzzy control, and the previous state-of-the-art reinforcement learning are simulated in IESs where the renewable energy generators output is 0 %, 20 %, 40 %, 65 %, and 80 % of all generators output, the results show the average frequency deviation, total generation cost, and carbon emission cost are at least 42.7 %, 0.92 %, and 0.93 % smaller than the comparison algorithm, respectively.

Innovation for recycling of organic matter through composter with automatic and sustainable temperature recording accessed via Bluetooth/mobile app.

Compost reactors, commonly used in experiments, industrial assays, and home residue treatment systems, have the potential to facilitate composting. Challenges persist in the realm of small-scale composting, encompassing facets such as temperature monitoring, homogenization of the compost mass, management of moisture with the control of leachate generation, and integration with a renewable energy source. This study assesses a pioneering composter prototype endowed with essential features to ensure a pragmatic and secure composting process. This includes the facilitation of remote access to temperature data via Bluetooth and a mobile application. Across successive trials, the scrutinized composter prototype consistently yielded reproducible outcomes, exhibiting a coefficient of variation below 25% for the majority of appraised parameters. In comparison to a conventional reactor, the decomposing residue mixture within the examined prototype manifested elevated temperatures (p < 0.05). Moreover, the tested prototype demonstrated C/N ratio lower than 20/1 within 45days, a higher final nitrogen concentration, and enhanced germination of seeds that served as phytotoxicity bioindicators. Notably, the prototype needed 46.6% less space, offering improved leachate control, three times faster turning time, temperature monitoring, and reduced fly attraction.

The design of a model predictive control strategy and performance analysis for pumped storage units and super capacitors in power systems

IntroductionSuper capacitors were regarded as one of the most effective frequency regulation technologies in power systems. The major issue of applying super capacitors for frequency regulation is the limited energy capacity and the high construction costs. The pumped storage units have been developed and applied in power systems for decades, whereas it was rarely operated to regulate the system frequency due to the operation limitations. The pumped storage units could be operated as a hydro generator under the generation mode and provide effective frequency regulation service with the conventional automatic generation control (AGC) system while its output power was constant under the pumping mode.MethodIn this paper, the pumped storage unit was controlled to participate in frequency regulation under the generation mode with super capacitors. Considering the AGC system could not consider the state of charging (SOC) of super capacitors for frequency regulation, an optimization model was proposed to determine the operation points for super capacitors and pumped storage units under a model predictive control (MPC) strategy to regulate the system frequency for power systems.Results and DiscussionBased on the various operation scenarios, the effectiveness and costs of pumped storage units and super capacitors to provide frequency regulation services were discussed. It was indicated that pumped storage units and super capacitors were effective technologies for power systems.

Energy management strategy and operation strategy of hybrid energy storage system to improve AGC performance of thermal power units

With the continuous increase of the renewable energy power generation penetration, the intermittency and fluctuation of renewable energy power generation make the traditional thermal power units (TPU) which play the main role of regulation need to undertake more frequent and more severe regulation tasks. In order to improve the automatic generation control (AGC) command response capability of TPU, an operation strategy of hybrid energy storage system (HESS) is proposed in this paper. While assisting TPU to complete the regulation tasks, it gives full play to the advantages of power-type and energy-type energy storage. Moreover, an energy management strategy of energy storage array (ESA) is proposed to improve the overall operation efficiency of ESA while making the state of charge (SOC) of all energy storage subunits consistent. Finally, the effectiveness of the proposed strategy is verified by simulation experiments. The results show that after using HESS to assist TPU operation, the comprehensive regulation performance index of TPU increased from 4.0198 to 7.8112, which significantly improved the operational flexibility of TPU. Meanwhile, the strategy proposed in this paper makes different types of energy storage systems in HESS operate in a relatively healthy SOC range, and the SOC of the flywheel energy storage system (FESS) reaches the limitation value decrease from 6 times to once, ensuring the overall charge and discharge capacity of HESS.

A viscoelastic harvester-absorber for energy generation and vibration control

Abstract A vibration energy harvester harnesses the oscillatory movement and&amp;#xD;converts it into electrical energy. On the other hand, a (dynamic) vibration absorber&amp;#xD;dissipates the mechanical energy of a host structure (primary system). The proposal for&amp;#xD;a harvester-absorber device (HAD), comprinsing a piezoelectric element (conventional&amp;#xD;harvester) bonded with a viscoelastic layer and a steel constrained layer, is based on the&amp;#xD;dual purpose of generating energy and at the same time reducing the vibratory response&amp;#xD;of the primary system. Introducing a viscoelastic dissipative layer in a conventional&amp;#xD;harvester, also offers the advantage of simultaneously reducing wear on the piezoelectric&amp;#xD;element, thereby extending its lifespan and preventing fatigue-related breakage. The&amp;#xD;paper presents a model of a harvester-absorber built from a bimorph piezoelectric&amp;#xD;beam, a layer of viscoelastic material (E-A-R C1002-01PSA) and a beam of steel, which&amp;#xD;acts as a constrained layer. A lumped parameter model is proposed for the theoretical&amp;#xD;description of the HAD which is successfully used to experimentally determine its&amp;#xD;parameters. After that, and using the equivalent stiffness concept, the HAD was&amp;#xD;coupled to a free-free beam with two masses at its ends to study its perfomance over a&amp;#xD;real host structure. The inertance of the composite system and voltage/force frequency&amp;#xD;response function of the HAD were obtained and compared with experimental results.&amp;#xD;The accuracy of the results justifies and validate the model which has the advantage&amp;#xD;of simplicity and can contribute to reducing wear and increasing the lifespan of brittle&amp;#xD;piezoelectric structures.

The Generation, Measurement, Prediction, and Prevention of Residual Stress in Nickel-Based Superalloys: A Review

As a crucial high-performance material, nickel-based superalloys inevitably generate residual stresses during processing, manufacturing, and usage. The mechanical properties of nickel-based superalloys are significantly reduced by residual stress, which becomes one of the important factors restricting material reliability. The systematic analysis of residual stresses in nickel-based superalloys throughout the entire manufacturing and usage processes is insufficient. The residual stress generation factors, measurement methods, prediction models, and control methods in nickel-based superalloys in recent years are summarized in this paper. The current challenge and future development trends in the research process of nickel-based superalloy residual stress are also presented. A theoretical reference for further research on residual stresses in nickel-based superalloys can be provided in this review.

Hybrid modeling with data enhanced driven learning algorithm for smart generation control in multi-area integrated energy systems with high proportion renewable energy

Multi-area integrated energy systems (MA-IES) with a high proportion of renewable energy have become a trend in social development. The access to a large number of distributed energy sources makes the safe, stable, and economic operation of MA-IES suffer serious challenges. To reduce the frequency deviation and area control error (ACE) of MA-IES and to improve the economy of operation, this study proposed a hybrid modeling with data enhanced driven learning algorithm, which combines model-driven and data-driven algorithms, named combined proportion-integral-derivative and deep reinforcement learning (CPIDDRL). Firstly, the frequency deviation signals are collected and judged, and the smaller frequency deviation signals, which are sent to the model-driven part, are regulated by proportion-integral-derivative (PID) controllers. Then, the ACE and large frequency deviation signals are collected and transmitted to the data-driven part, and the Transformer deep learning network is applied to predict the multi-feature timeseries, which are applied to strategy selection for state-action-reward-state-action reinforcement learning. Finally, the model-driven part and data-driven part work together to generate adjustment commands every 4 s. The control effects of CPIDDRL, PID, Q-learning, and sliding model control algorithms are compared in two and four areas integrated energy systems. The results show that, compared to the comparison algorithms, the CPIDDRL reduces the mean values of frequency deviation and ACEs by at least 46.78% and 6.83%, respectively, and the total generation cost by at least 8.20%. CPIDDRL has practical significance in improving system stability, enhancing renewable energy integration capabilities, and promoting the development of smart grids.

A Bi‐level stacked LSTM‐DNN‐based decoder network for AGC dispatch under regulation market framework in presence of VPP and EV aggregators

AbstractThe consideration of mileage settlement in the frequency regulation market has encouraged fast‐acting units, such as converter‐interfaced generators (CIG) and electric vehicle stations, to actively participate in load‐generation balancing through automatic generation control (AGC). Conventional frequency regulation faces challenges in coping with the growing variability of CIGs and also lacks effective incentives for rapid‐responding units. In this context, a bi‐level AGC dispatch approach based on a stacked long short‐term memory (LSTM)‐deep neural network (DNN)‐based decoder framework is proposed for a power system comprising diverse CIGs forming a virtual power plant and electric vehicle aggregators. The proposed decoder network is comprised of stacked LSTM and DNN, wherein the cascaded LSTM layers are introduced to accurately capture temporal information from time series input. The inclusion of a dropout mechanism further enhances the model’s generalisability in unforeseen environments. The proposed dispatch framework uses mileage‐based compensation criteria to optimally allocate instructions among various participating units with differing regulation characteristics. The performance of the proposed method is analysed by considering packet loss, delay, unexpected generation failure, and denial of service attacks. The evaluation of the proposed approach reveals its superior performance compared to proportionality, particle swarm optimisation, decision tree, and DNN methods.