Power Generation Control Research Articles

MIVNDN: Ultra-Short-Term Wind Power Prediction Method with MSDBO-ICEEMDAN-VMD-Nons-DCTransformer Net

Wind speed, wind direction, humidity, temperature, altitude, and other factors affect wind power generation, and the uncertainty and instability of the above factors bring challenges to the regulation and control of wind power generation, which requires flexible management and scheduling strategies. Therefore, it is crucial to improve the accuracy of ultra-short-term wind power prediction. To solve this problem, this paper proposes an ultra-short-term wind power prediction method with MIVNDN. Firstly, the Spearman’s and Kendall’s correlation coefficients are integrated to select the appropriate features. Secondly, the multi-strategy dung beetle optimization algorithm (MSDBO) is used to optimize the parameter combinations in the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) method, and the optimized decomposition method is used to decompose the historical wind power sequence to obtain a series of intrinsic modal function (IMF) components with different frequency ranges. Then, the high-frequency band IMF components and low-frequency band IMF components are reconstructed using the t-mean test and sample entropy, and the reconstructed high-frequency IMF component is decomposed quadratically using the variational modal decomposition (VMD) to obtain a new set of IMF components. Finally, the Nons-Transformer model is improved by adding dilated causal convolution to its encoder, and the new set of IMF components, as well as the unreconstructed mid-frequency band IMF components and the reconstructed low-frequency IMF, component are used as inputs to the model to obtain the prediction results and perform error analysis. The experimental results show that our proposed model outperforms other single and combined models.

Modelling of Biomass Gasification Through Quasi-Equilibrium Process Simulation and Artificial Neural Networks

In the past two decades, advancements in thermochemical technologies have improved biomass gasification for distributed power generation, enhancing efficiency, scalability, and emission control. This study aims to optimize syngas production from biomass gasification by comparing two computational models: a quasi-equilibrium thermodynamic model implemented in Aspen Plus and an artificial neural network (ANN) model. Operating at 850 °C with varying steam-to-biomass (S/B) ratios, both models were validated against experimental data. Results show that hydrogen concentration in syngas increased from 19.96% to 43.28% as the S/B ratio rose from 0.25 to 0.5, while carbon monoxide concentration decreased from 24.6% to 19.1%, consistent with the water–gas shift reaction. The ANN model provided rapid predictions, showing a mean absolute error of 3% for hydrogen and 2% for carbon monoxide compared to experimental data, though it lacks thermodynamic constraints. Conversely, the Aspen Plus model ensures mass and energy balance compliance, achieving a cold gas efficiency of 95% at an S/B ratio of 0.5. A Multivariate Statistical Analysis (MVA) further clarified correlations between input and output variables, validating model reliability. This combined modelling approach reduces experimental costs, enhances gasification process control and offers practical insights for improving syngas yield and composition.

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

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.

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.

Thermal management and power generation characteristics in a bifurcating channel by using a piezo-embedded inclined elastic fin assembly during turbulent forced convection of ternary nanofluid

Numerous engineering systems use piezoelectric energy harvesters (PE-EHs), which have several benefits including low cost, simplicity, better power density, and ease of installation. They can be used in different applications for energy harvesting and different external sources such as wind and vortex induced vibrations due to fluid–structure interaction can be utilized. This study uses a unique elastic fin PE/EH assembly for power generation, thermal management, and flow control in a bifurcating channel. Performance of the system is improved by using ternary nanofluid in the channel cooling system. Galerkin weighted residual FEM with ALE is used as the solution method. The convective heat transfer performance and power generation by the EH device are investigated in relation to the varying following parameters: Reynolds number (Re between 10000 and 30000), PE-EH inclination (γ between 0 and 60), fin horizontal location (xf between −H and H), and nanoparticle loading in the base fluid (ϕ between 0 and 0.03). The vortex size and distribution near the junction are significantly influenced by the fin inclination and horizontal location of the fin assembly within the bifurcating channel. When varying other parameters of interest, using nanofluid at the highest loading results in significant deflection of the fin assembly and power generation within the PE-EH. Enhancement factors (EFs) for power generation becomes 15.6 and 39.8 when cases of lowest and highest Re are compared with pure fluid and nanofluid while they are 2.93 and 3.38 for thermal performance improvements. Fin inclination of γ=30 is found as the optimum inclination for achieving the highest power from the assembly. Higher inclination of elastic fin/PE-EH assembly results in cooling performance deterioration while average Nu reduces by a factor of 2.94 by varying inclination from γ=30 to γ=60 with nanofluid. For power generation, effects of using nanofluid with varying inclination is significant and EF becomes 252 from γ=0 to γ=30. There are opposing tendencies for the device’s power generation and cooling performance enhancement when the fin’s horizontal placement is changed. EF for average Nu is 7.25 for varying fin location. As the loading of nanoparticle inside the base fluid increases, the average Nu and generated power exhibit non-linear rising characteristics. Polynomial type correlations are provided for the average Nu and power from the device by varying elastic fin assembly inclination and nanoparticle solid volume fraction. Potential of using hybrid nanofluid in PE-EH embedded thermo-fluid system is shown. The design, development, and optimization of self-sufficient power production systems that may be used to various thermo-fluid systems, such as electronic cooling and thermal control in a range of heat transfer devices, may benefit from the findings.

Fault current limiting control of full-scale wind power generators based on switched bang-bang scheme

Fault current limiting control of full-scale wind power generators based on switched bang-bang scheme

Construction of a photovoltaic low-voltage grid connected power regulation system based on partition control

Abstract Photovoltaic power generation is a green and renewable power generation mode and has been widely used. However, the power fluctuation of photovoltaic power generation has a significant impact on the power grid, which is very important. Based on the concept of regional control, this paper studies the photovoltaic low-voltage grid-connected power regulation system. The photovoltaic power generation system is divided into multiple regions, and each region is provided with an independent control unit and regulation strategy under the control of multiple control areas. The regional factors are combined, such as light intensity and power voltage. In the case of the city, the impact of the area on the whole is dynamically adjusted to achieve the overall balance of power and the stability of the system. The experimental results show that the photovoltaic low-voltage grid-connected power regulation system constructed by regional control performs well and has a high degree of stability. According to the change in light intensity and load demand, the system can achieve accurate control and distribution of photovoltaic power generation.

Improved machine learning-based pitch controller for rated power generation in large-scale wind turbine

In variable speed and variable pitch large-scale wind turbines, the pitch controller plays a crucial role in optimizing power output near the rated value during wind speeds that exceed the rated threshold. Nevertheless, the erratic nature of wind speeds poses challenges to the pitch controller’s efficacy, leading to a decline in generator power. So, there has been a growing interest among researchers in the development of machine learning-based pitch controllers. This paper introduces an improved recurrent radial basis function neural network and its parameters were tuned using the modified particle swarm optimization algorithm to enhance neural network performance. The proposed controller is validated in a benchmark wind turbine and comparative analysis are conducted against existing controllers in the literature. Through a series of comprehensive studies, the proposed controller consistently outperforms its counterparts, particularly in achieving power output close to the rated value.

Improved robust model predictive control for residential building air conditioning and photovoltaic power generation with battery energy storage system under weather forecast uncertainty

The rising demands for comfort alongside energy conservation underscore the importance of intelligent air conditioning control systems. Model Predictive Control (MPC) stands out as an advanced control strategy capable of addressing these demands. However, accurate prediction of all relevant variables remains a challenge in practical scenarios, complicating MPC’s ability to devise effective control actions amid prediction inaccuracies. To counteract this issue, this paper introduces an enhanced Double-Layer Model Predictive Control (DLMPC) algorithm. This innovative approach adjusts for discrepancies between forecasted and actual values without the need for additional variables and models, thereby reducing the adverse effects of prediction errors. Additionally, we develop precise models for room temperature simulation and for calculating air conditioning (AC) load and energy consumption, grounded in empirical data from residential settings and AC performance tests. Validation of these models demonstrates their efficacy in enabling MPC to formulate efficacious control strategies. When juxtaposed with a baseline model, the DLMPC algorithm significantly improves temperature regulation accuracy by up to 15.12% and achieves a 10.50% reduction in energy consumption over the heating season.

International Cooperation as a Component of Japan's Energy Security

The importance of international cooperation is considered in the context of ensuring Japan's energy security. The concept of energy security and its importance for the sovereignty of the state is analyzed. The role of international energy relations in the modern world is analyzed, and the strategic partnerships that Japan is developing to ensure sustainable access to resources are highlighted. Special attention is paid to Japan's diplomatic efforts aimed at diversifying energy sources, ensuring technological exchange and promoting the development of energy-efficient solutions. Japan has historically considered international cooperation as one of the ways to solve its energy problems. The country is a member of many international organizations and actively participates in international conferences and initiatives. Japan is not only a stable importer of resources, but also an exporter of various energy technologies. Japanese electric power companies operate all over the world. Each of Japan's electric power companies has individual agreements with foreign companies to share a wide range of information including that on power generation, distribution and quality control. Due to the fact that Japan does not have a significant raw material base for the development of industry and meeting energy needs, the main tool for solving the country's resource problems is to build active long-term relationships with resource-rich countries. The conducted research shows that ensuring stable supplies of energy resources has been the cornerstone of Japan's economic diplomacy for many decades. The article emphasizes the importance of global cooperation in the field of energy for ensuring sustainable development and ensuring national energy security in the face of modern challenges and changes in the global energy paradigm.

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

Introduction: With the rapid development of human society and economy, the power generation technology of various new energy vehicles has begun to receive widespread attention.Methods: Due to the lack of inertia and frequency stability in the new energy vehicle power generation system, this paper proposes a power generation control method that combines linear active disturbance rejection control technology and virtual synchronous generator technology. This method first introduces the control strategy and inertial response of the virtual synchronous generator. Then, it uses linear active disturbance rejection control technology to improve the virtual synchronous generator technology to deal with the uncertainty and external interference in the system.Results: The results showed that when the virtual inertia coefficient was 0, and the new energy vehicles would hardly intervene in the regulation of the grid voltage. When the virtual inertia coefficient was 5, the decline rate of the DC bus voltage of new energy vehicles had slowed down. When the virtual inertia coefficient increased, the power output of new energy vehicles can be increased to the grid. When the load suddenly increased, and the corresponding DC bus voltage decreased more slowly. In the VSG output power comparison, under the research method, the frequency fluctuation only increased by 0.09 Hz and returned to the rated frequency of 50 Hz. Additionally, the dynamic process of the system output power was the shortest, lasting only 0.05 s.Discussion: The above results show that the research method has significant superiority and effectiveness in improving the inertial response and overall stability of the new energy vehicle power system.

Opportunities and Challenges in AI Based Modern Power System

Nowadays, the power grid has transformed into a dynamic and extensive resource generation and management system. This transformation is primarily driven by the widespread adoption of renewable energy sources and the utilization of intelligent information and communication technologies to handle dynamic workloads. The smart grid encompasses various innovative operations, including power electrification, intelligent information integration at the physical layer, and intricate interconnections. These operations leverage data-driven deep learning, big data, and machine learning paradigms to effectively analyze and control transient issues within the electric power system. Artificial intelligence (AI) has emerged as a crucial tool in addressing and resolving challenges associated with transient stability assessment (TSA) and power generation control. In this research paper, we present a comprehensive review that explores the role of AI and its sub-procedures in tackling TSA problems. The article outlines an AI-based intelligent power system structure, along with the rationality of applying AI to transient situations in power system TSA. Distinguishing itself from other reviews, this paper delves into the AI-based TSA framework and design process, highlighting intelligent applications and their analytical capabilities in addressing power system transient problems. Furthermore, our analysis extends beyond AI alone, as we also explore the integration of big data, which aligns seamlessly with AI. The paper discusses future trends, opportunities, challenges, and open issues pertaining to AI-Big data based transient stability assessment in the smart power grid.

Modeling of flexible AC transmission system devices and fuzzy controller for automatic generation control of electric vehicle-injected power system

The automatic generation control (AGC) is a paradigm topic of interest that is continuously evolving for adequate power generation control in the power system. In this proposed study, a fuzzy adapted PID (FPID) controller is assigned for the AGC of the hybrid power system in various electrical risks and hazards. The hybrid power system includes traditional thermal and hydroelectric facilities along with gas and nuclear power plants. Two FACTS components, the superconducting magnetic energy storage (SMES) and the interline power flow controller (IPFC), have been included in to the system to improve stability of the system. The proposed FPID controller is developed to its full potential using the newly developed teaching learning-based optimization (TLBO) method. The TLBO technique's capability has been compared against a few common evolutionary strategies in order to demonstrate its superiority. It also shows how FPID controllers outperform traditional PID controllers. Investigations are also done on how IPFC and SMES might improve system stability performance. Owing to the outcome study, it is revealed that suggested TLBO-FPID, IPFC and SMES are most effective tool to advance the AGC mechanism. It is revealed from the outcomes that anticipated FPID controller reduces the settling time of area1 frequency (ΔF1) by 135.72 %, 428.58 % and 471.42 % over TLBO;PID, DE;PID and PSO;PI respectively. Finally, the proposed mechanisms are discussed with employing in electric vehicle integrated three-area distributed energy source-based power network to justify the efficacy of the proposed approaches.

Utilisation of Deep Learning (DL) and Neural Networks (NN) Algorithms for Energy Power Generation: A Social Network and Bibliometric Analysis (2004-2022)

The research landscape on the applications of advanced computational tools (ACTs) such as machine/deep learning and neural network algorithms for energy and power generation (EPG) was critically examined through publication trends and bibliometrics data analysis. The Elsevier Scopus database and the PRISMA methodology were employed to identify and screen the published documents, whereas the bibliometric analysis software VOSviewer was used to analyse the co-authorships, citations, and keyword occurrences. The results showed that 152 documents have been published on the topic comprising conference proceedings (58.6%) and articles (41.4%) between 2004 and 2022. Publication trends analysis revealed the number of publications increased from 1 to 31 or by 3,000% over the same period, which was ascribed to the growing scientific interest and research impact of the topic. Stakeholder analysis revealed the top authors/researchers are Anvari M, Ghaderi SF and Saberi M, whereas the most prolific affiliation and nations actively engaged in the topic are the North China Electric Power University, and China, respectively. Conversely, the top funding agency actively backing research on the topic is the National Natural Science Foundation of China (NSFC). Co-authorship analysis revealed high levels of collaboration between researching nations compared to authors and affiliations. Hotspot analysis revealed three major thematic focus areas namely; Energy Grid Forecasting, Power Generation Control, and Intelligent Energy Optimization. In conclusion, the study showed that the application of ACTs in EPG is an active, multidisciplinary, and impact area of research with potential for more impactful contributions to research and society at large.

Intelligent Decision Algorithm for Photovoltaic Power Generation Control Based on Feature Neural Computing Modeling

Abstract The integration of renewable energy sources into the power grid, especially photovoltaic (PV) systems, has seen a significant upsurge due to the global push for sustainable energy. However, the variable nature of solar energy poses unique challenges in the effective management and control of PV power generation. This study introduces an innovative approach to intelligent decision-making in PV power generation control, leveraging deep learning techniques for temporal data feature modeling. By collecting and analyzing time-series data from PV systems, the proposed method utilizes feature neural computing to model the inherent characteristics of the data, facilitating effective temporal feature classification. The classification results are then employed to inform intelligent control strategies, optimizing the efficiency and reliability of PV power generation. This article proposes an end-to-end neural network structure that can simultaneously mine multi-scale local information and global temporal correlation information in sequences. This structure is primarily achieved through a module. The module consists of two parallel branches. One branch improves the InceptionTime module with depthwise separable convolution to extract multi-scale subsequence information. The other branch employs multi-head self-attention technology with added layer positional encoding to extract global temporal correlation information in sequences.

IoT-based Deep Learning Neural Network (DLNN) algorithm for voltage stability control and monitoring of solar power generation

Today, Solar Photovoltaic (SPV) energy, an advancing and attractive clean technology with zero carbon emissions, is widely used. It is crucial to pay serious attention to the maintenance and application of Solar Power Generation (SPG) to harness it effectively. The design was more costly, and the automatic monitoring is not precise. The main objective of the work related to designed and built up the Internet of Things (IoT) platform to monitor the SPV Power Plants (SPVPP) to solve the issue. IoT platform designing and Data Analytics (DA) are the two phases of the proposed methodology. For building the IoT device in the IoT platform designing phase, diverse lower-cost sensors with higher end-to-end delivery ratio, higher network lifetime, throughput, residual energy, and better energy consumption are considered. Then, Sigfox communication technology is employed at the Low-Power Wireless Area Network (LPWAN) communication layer for lower-cost communication. Therefore, in the DA phase, the sensor monitored values are evaluated. In the analysis phase, which is the most significant part of the work, the input data are first pre-processed to avoid errors. Next, to monitor the Energy Loss (EL), the fault, and Potential Energy (PE), the solar features are extracted as of the pre-processed data. The significance of utilizing the Transformation Search centered Seagull Optimization (TSSO) algorithm, the significant features are chosen as of the extracted features. Therefore, the computational time of the solar monitoring has been decreased by the Feature Selection (FS). Next, the features are input into the Gaussian Kernelized Deep Learning Neural Network (GKDLNN) algorithm, which predicts the faults, PE, and EL. In the experimental evaluation, solar generation is assessed based on Wind Speed (WS), temperature, time, and Global Solar Radiation (GSR). The systems are satisfactory and produce more power during the time interval from 12:00 PM to 1:00 PM. The performance of the proposed method is evaluated based on performance metrics and compared with existing research techniques. When compared to these techniques, the proposed framework achieves superior results with improved precision, accuracy, F-measure, and recall.

Quantum-inspired distributed policy-value optimization learning with advanced environmental forecasting for real-time generation control in novel power systems

With the increasing weight of wind and photovoltaic power (WPP) in grids, the uncertainty of WPP has an increasing impact on power systems. The access to WPP raises the difficulty of controlling the grid frequency. The existing automatic generation controllers have difficulty in reducing the impact of a high proportion of WPP integration on grid frequency, difficulty in autonomously adjusting power balance in different areas of distributed novel power systems, and slow system operation speed. To address instability, weak adaptability, and slow processing rate caused by large-scale WPP access to novel power systems, this study proposes the quantum-inspired distributed policy-value optimization learning methods for power generation control in distributed systems and adopts the advanced environmental forecasting method into the multi-temporal distance forecasting-distributed Grover policy-value optimization (MF-DGPVO) methods. In this study, the MF-DGPVO methods combine quantum thought with the training process of value optimization and policy optimization to reduce data operations and improve network operation speed. In this study, the MF-DGPVO methods are simulated in the two- and four-area novel power grids. Compared to proportional-integral-derivative, Q learning (QL), state-action-reward-state-action (SARSA), sliding mode control, and fuzzy logic control, the MF-DGPVO methods are more than 30% smaller in frequency deviations and more than 50% smaller in control errors. Besides, the proposed MF-DGPVO approaches obtain greater flexibility than QL and SARSA.

Performance Evaluation of Rainfall-Runoff Models for Predictions of Inflows to Bhumibol Reservoir in Thailand

The Bhumibol reservoir in the Ping River basin is the largest reservoir in the Kingdom of Thailand. This reservoir has contributed to economic development of the country by supplying increased electricity and irrigation water demands as well as flood mitigation in riparian areas along the Ping and the Chao Phraya River. The prediction of inflows to the reservoir is crucial for the optimal management of water for irrigation, power generation and flood control. Properly customized rainfall-runoff models of the catchment could provide the basis for predicting the inflows to the reservoir. Hence, five lumped conceptual rainfall-runoff models were developed for the Ping River basin to simulate daily inflows to the Bhumibol reservoir. The rainfall-runoff models are Australian Water Balance Model (AWBM), Sacramento Soil Moisture Accounting Model, Simplified Hydrolog Model (SIMHYD), Soil Moisture Accounting and Routing Model (SMAR) and Tank Model. The evaluation of the performances of these models showed that all models are capable of predicting inflows. However, the SIMHYD, Sacramento, AWBM and Tank models perform better than SMAR model. Hence, these models could be employed for prediction of inflows to the reservoir with acceptable accuracy.

Adaptive optimal secure wind power generation control for variable speed wind turbine systems via reinforcement learning

As the utilization of wind energy continues to grow, it is crucial to prioritize the identification of vulnerabilities, raise awareness, and develop strategies for cybersecurity defense. False data injection (FDI) attacks, if targeted at the communication between the rotor speed sensor and the wind turbine (WT) controller, can potentially disrupt the normal operation of the system. These attacks have the capability to overload the drive-train and significantly reduce the power generation efficiency of the wind turbine. So, this note presents an adaptive optimal secure control strategy entailing reinforcement learning (RL) neural network (NN) using the filter error to compensate the detrimental effects of FDI attack as well as actuator fault for WT systems. The Hamilton–Jacobi–Bellman (HJB) equation is constructed and solved to obtain the optimal control policy. Since the HJB is inextricably intertwined with intrinsic nonlinearity and complexity, solving this equation is quiet challenging. To tackle this issue and also approximate the solution of the HJB, an actor–critic-based reinforcement learning (RL) strategy is used, in which actor and critic NNs are applied to execute control action and assess control performance, respectively. To detect FDI attack, an anomaly detection is developed using a nonlinear observer/estimator. Stability analysis is performed using Lyapunov theory which guarantees semi-global uniformly ultimately bounded (SGUUB) of the error signal. Finally, simulation results verify the effectiveness of the proposed control approach.