Power System Stability Research Articles

Short-Term Electricity Load Forecasting Based on Improved Data Decomposition and Hybrid Deep-Learning Models

Short-term power load forecasting plays a key role in daily scheduling and ensuring stable power system operation. The problem of the volatility of the power load sequence and poor prediction accuracy is addressed. In this study, a learning model integrating intelligent optimization algorithms is proposed, which combines an ensemble-learning model based on long short-term memory (LSTM), variational modal decomposition (VMD) and the multi-strategy optimization dung beetle algorithm (MODBO). The aim is to address the shortcomings of the dung beetle optimizer algorithm (DBO) in power load forecasting, such as its time-consuming nature, low accuracy, and ease of falling into local optimum. In this paper, firstly, the dung beetle algorithm is initialized using a lens-imaging reverse-learning strategy to avoid premature convergence of the algorithm. Second, a spiral search strategy is used to update the dynamic positions of the breeding dung beetles to balance the local and global search capabilities. Then, the positions of the foraging dung beetles are updated using an optimal value bootstrapping strategy to avoid falling into a local optimum. Finally, the dynamic-weighting coefficients are used to update the position of the stealing dung beetle to improve the global search ability and convergence of the algorithm. The proposed new algorithm is named MVMO-LSTM. Compared to traditional intelligent algorithms, the four-quarter averages of the RMSE, MAE and R2 of MVMO-LSTM are improved by 0.1147–0.7989 KW, 0.09799–0.6937 KW, and 1.00–13.05%, respectively. The experimental results show that the MVMO-LSTM proposed in this paper not only solves the shortcomings of the DBO but also enhances the stability, global optimization capability and information utilization of the model.

Optimally tuned cascaded FOPI-FOPIDN with improved PSO for load frequency control in interconnected power systems with RES

In the operation and control of power systems, load frequency control (LFC) plays a critical role in ensuring the stability and reliability of interconnected power systems. Modern power systems with significant penetration of highly variable and intermittent renewable sources present new challenges that make traditional control strategies ineffective. To address these new challenges, this paper proposes a novel LFC strategy that employs a cascaded fractional-order proportional integral-fractional-order proportional integral derivative with a derivative filter (FOPI-FOPIDN) as a controller. The parameters of the FOPI-FOPIDN are optimised using a variant of the particle swarm optimization (PSO) in the literature called ADIWACO. The effectiveness and scalability of the proposed strategy are validated by extensive simulations conducted on two- and three-area test systems and performance comparisons with recent LFC control strategies in the literature. The performance metrics used for the evaluation are ITAE values, deviations in the power flows in the tie-lines, and deviations in the frequencies of the control areas with the power systems subjected to diverse load and RES generation disturbances in several experimental scenarios. Governor dead band, communication time delay, and generation rate constraints are considered in one of the scenarios for more realistic evaluation. Again, the controller’s robustness to uncertain model parameters is validated by varying the parameters of the three-area test system by ± 50%. The simulation results obtained confirm the controller’s robustness and its superiority over the comparison LFC strategies in terms of the above performance metrics.

Line to Ground Fault Detector in 400 KV Transmission Line by Using Intelligent System

The ability to locate and identify problems in power transmission lines is the most crucial component of power system engineering since it ensures the stable and efficient operation of electrical grids. to minimize downtime, prevent cascading failures, and maintain the power system's stability. problem localization helps identify the precise location of the problem and speeds up the process of restoring the power supply while minimizing the impact on consumers. This paper presents a measurement-based automatic fault localization and detection (ANFIS) technique for transmission lines. High-speed processing of real-time error localization and detection is made possible by the design and implementation of ANFIS. It is suggested that ANFIS for digital distance protection systems be able to discover issues in addition to detecting them. When a transmission experiences a three-phase malfunction Thus, the suggested method can precisely pinpoint the impacted stages. ANFIS has been trained and tested using many kinds of field data. The field data are collected by simulating faults in the Simulink Matlab model depicting the transmission line at various sites between Misan – Kut station 400 Kv along 200 Km using computer software based on Matlab. Phase current and voltage measurements are utilized as ANFIS inputs and are available at the busses. Regarding the kind and detection of defects, The output will demonstrate fault and location identification with an extremely low error percentage through simulated processes; the results also demonstrate that the approach's selectivity and speed are quite dependable and provide adequate performance for applications involving transmission and distribution monitoring; and security. The study emphasizes the criticality of promptly and precisely locating faults in power transmission lines to preserve the stability and security of the power system.

An allocative method of stationary and vehicle‐mounted mobile energy storage for emergency power supply in urban areas

AbstractExtreme climate events are on the rise, posing significant challenges to power systems, leading to blackouts and infrastructure damage. Energy storage plays a crucial role in enhancing grid resilience by providing stability, backup power, load shifting capabilities, and voltage regulation. While stationary energy storage has been widely adopted, there is growing interest in vehicle‐mounted mobile energy storage due to its mobility and flexibility. This article proposes an integrated approach that combines stationary and vehicle‐mounted mobile energy storage to optimize power system safety and stability under the conditions of limiting the total investment in both types of energy storages. The principal aim is to minimize the weighted energy not served index in the presence of fault conditions. By strategically allocating energy storage resources and dynamically dispatching stored energy, operators can ensure rapid response and effective power restoration, improving overall reliability in the face of extreme weather events.

Frequency stability of new energy power systems based on VSG adaptive energy storage coordinated control strategy

A self-adaptive energy storage coordination control strategy based on virtual synchronous machine technology was studied and designed to address the oscillation problem caused by new energy units. By simulating the characteristics of synchronous generators, the inertia level of the new energy power system was enhanced, and frequency stability optimization was achieved. This strategy is integrated with the frequency response model of the new energy power system to improve the system's frequency regulation capability and achieve more stable and efficient operation. From the results, the damping of the system increased, the oscillation frequency decreased after a duration of about 15 s, and the system stability improved by 76.09%. The proposed strategy based on virtual synchronous generator adaptive energy storage coordination control strategy was improved by 83.25%. In addition, the proposed strategy has improved stability indicators and system completion efficiency by 40.57% and 22.21% respectively, both of which are better than the comparative strategies. As a result, this strategy significantly enhances the frequency regulation capability of the system, which has a positive effect on achieving efficient operation of the new energy power system and maintaining the stability of the power system.

On the optimal multi-objective design of fractional order PID controller with antlion optimization

This study introduces an optimal multi-objective design approach for a robust multimachine Fractional Order PID Controller (FOPID) using the Antlion algorithm. The research focuses on the need for effective stabilizers in multimachine power systems by employing traditional speed-based lead-lag FOPID controllers. The study formulates a multi-objective problem, optimizing the damping factor and damping ratio of lightly damped electromechanical modes to maximize a composite set of objective functions, tackled through the Antlion algorithm. Stability analysis of Single-Machine Infinite-Bus (SMIB) and multimachine power systems is conducted based on rotor speed and power deviation minimization in the time domain response, along with damping ratio and eigenvalue analysis. The proposed approach is implemented and tested on three IEEE test cases, showcasing significant improvements in stability through the reduction of maximum overshoot (Mp) and settling time (ts) of speed deviation. Comparative analysis with other optimization-based FOPID controllers underscores the superiority of the proposed approach in enhancing stability in multimachine power systems. The main impact of this research lies in its contribution to the advancement of stability enhancement techniques in multimachine power systems, offering a systematic framework for optimal FOPID controller design and empowering decision-making processes in power engineering.

Quantitative assessment of static voltage stability for power system with high-penetration wind power based on energy function

As the scale of power systems expands, the penetration of renewable energy sources and power electronic devices are accelerating. With the growing complexity of power system, there is an urgent need for in-depth research on the impact of reactive power on static voltage stability. This paper proposes a quantitative assessment approach of static voltage stability for the power system with high-penetration wind power based on the energy function. A quantitative assessment framework for voltage stability indices based on power flow analysis has been established. The classic static voltage stability index L based on reactive power has been comprehensively analyzed. Additionally, combining the energy function and power flow analysis, a new quantitative index for voltage stability based on bus reactive power injection has been proposed. Finally, the effectiveness of this index has been validated by case studies. With the increasing penetration of wind power, the trend of the change of the assessment index of voltage margin is analyzed.

Improved chaotic Bat algorithm for optimal coordinated tuning of power system stabilizers for multimachine power system

Power systems exhibit nonlinearity. causing dynamic instability and complex power oscillations. This research proposes an innovative strategy using the Novel Bat Algorithm (NBA) to achieve ideal Power System Stabilizers (PSSs) in a multimachine power system. The approach shifts electromechanical modes to specific areas in the s-plane. Enhancing the multi-machine power system and establishing stabilizer parameters for dynamic performance. The study examines the designed approach aptitude for standard lead-lag PSSs configurations. In order to elevate the global search problem and transfer some static operators for the optimum optimization process. the chaos mapping. also known as CNBA. is introduced into NBA. Four different forms of chaos maps are compared in experiments to resolve unconstrained mathematical issues in order to illustrate CNBA performance. In any other case. the challenge of designing PSS under a wide range of loading situations is transformed into an optimization challenge with the damping ratio of electromechanical modes with low damping as the target function. The optimal stabilizers’ gains are gotten by employing the CNBA algorithm. Second plan. an effective technique is astutely established to delineate the PSS location and quantity using CNBA and another side using participation factor. To examine the efficacy of the proposed CNBA-based PSS on a large system; it is tested on the interconnected of New-England/New-York (16 generators and 68 buses) power grid. and verified by comparative study with NBA through eigenvalue analysis and nonlinear simulation to provide evidence the algorithmic competence of CNBA. The CNBA approach yields a minimum damping ratio of 37%. which is consistent with the its eigenvalue. In contrast, the NBA approach achieves a minimum damping ratio of 31%. The simulation results reveal the fine performance of the proposed CNBA-PSS in a convincing manner and its capacity to provide an excellent damping for inter-area and local oscillations under diverse operating cases compared to NBA-PSS then in the case of PSS location.

Surface reaction mechanisms research of Cun and Pdn (n=1–3) clusters modified MoSTe for fault gases in oil-immersed transformers

Efficient detection and timely resolution of internal faults within synthetic ester oil transformers are imperative for ensuring the stability of power systems. This paper proposes the utilization of MoSTe monolayer modified with Cun and Pdn (n=1–3) clusters for detecting internal fault gases (C2H2, C2H4, and CH4) in transformers. The adsorption mechanism and electronic properties of target gases on the modified substrate surface are systematically studied through density functional theory analysis of adsorption energy, density of states, and deformation charge density. The results indicate that TMn clusters possess the capability to enhance the reactivity of the MoSTe surface, leading to varying degrees of improvement in conductivity and adsorption capacity. Different target gases exhibit distinct adsorption characteristics, with the modified monolayer showing strong adsorption capabilities for C2H2 and C2H4. The calculation of work function and recovery time further reveal the promising potential of Pdn-MoSTe monolayer as resistive gas sensing materials for detecting C2H2 and C2H4, while Cun-MoSTe monolayer are also identified as high-quality candidates for C2H4 sensing materials. This study is expected to facilitate the practical application of MoSTe monolayer in the field of transformer operational state monitoring, providing new insights for exploring novel gas sensing materials.

Power system stabilizers tuning for probabilistic small-signal stability enhancement using particle swarm optimization and unscented transformation

Power system stabilizers tuning for probabilistic small-signal stability enhancement using particle swarm optimization and unscented transformation

Improving load frequency controller tuning with rat swarm optimization and porpoising feature detection for enhanced power system stability

Load frequency control (LFC) plays a critical role in ensuring the reliable and stable operation of power plants and maintaining a quality power supply to consumers. In control engineering, an oscillatory behavior exhibited by a system in response to control actions is referred to as “Porpoising”. This article focused on investigating the causes of the porpoising phenomenon in the context of LFC. This paper introduces a novel methodology for enhancing the performance of load frequency controllers in power systems by employing rat swarm optimization (RSO) for tuning and detecting the porpoising feature to ensure stability. The study focuses on a single-area thermal power generating station (TPGS) subjected to a 1% load demand change, employing MATLAB simulations for analysis. The proposed RSO-based PID controller is compared against traditional methods such as the firefly algorithm (FFA) and Ziegler-Nichols (ZN) technique. Results indicate that the RSO-based PID controller exhibits superior performance, achieving zero frequency error, reduced negative peak overshoot, and faster settling time compared to other methods. Furthermore, the paper investigates the porpoising phenomenon in PID controllers, analyzing the location of poles in the s-plane, damping ratio, and control actions. The RSO-based PID controller demonstrates enhanced stability and resistance to porpoising, making it a promising solution for power system control. Future research will focus on real-time implementation and broader applications across different control systems.

Fault detection and classification in overhead transmission lines through comprehensive feature extraction using temporal convolution neural network

AbstractFaults in transmission lines cause instability of power system and result in degrading end users sophisticated equipment. Therefore, in case of fault and for the quick restoration of problematic phases, reliable and accurate fault detection and classification techniques are required to categorize the faults in a minimum time. In this work, 500 kV transmission line (Jamshoro‐New Karachi), Sindh, Pakistan has been modeled in MATLAB. The discrete wavelet transform (DWT) has been used to extract features from the transient current signal for different faults in 500 kV transmission line under various parameters such as fault location, fault inception angle, ground resistance and fault resistance and time series data has been obtained for fault classification. Moreover, the temporal convolutional neural network (TCN) is used for fault classification in 500 kV transmission network due to its robust framework. From simulation results, it is found that faults in 500 kV transmission line are classified with 99.9% accuracy. Furthermore, the simulation results of the TCN model compared to bidirectional long short‐term memory (BiLSTM) and Gated Recurrent Unit (GRU) and it has been found that TCN model is capable of classifying faults in 500 kV transmission line with high accuracy due to its ability to handle long receptive field size, less memory requirement and parallel processing due to dilated causal convolutions. Through this work, the meantime to repair of 500 kV transmission line can be reduced.

Universal SSO Stabilization of Renewable Energy Power Systems

Universal SSO Stabilization of Renewable Energy Power Systems

Power System Stabilizer Based on Artificial Bee Colony Algorithms

Power System Stabilizer Based on Artificial Bee Colony Algorithms

Applications of Multi-Objective OPF Solutions with Optimal Placement of Multiple and Multi-Type FACTS Units to IEEE System: Comparison of Different Approaches

Optimal power flow (OPF) problem and its implications for power system stability and efficiency is investigated in this study. OPF, a restricted optimization query with non-linearity and non-convexity, is one of the most challenging and fascinating problems in the recent power system. Based on these parameters, researchers have been working hard over the past few decades to identify the best solutions to the OPF issue that maintain system stability. This work presents multi-objective OPF solutions utilizing Newton's technique with numerous multi-type FACTS units. First, the GA is applied to identify the perfect size and location of the FACTS units. Next, the generator and FACTS settings are optimized. In this instance, four scenarios are taken into consideration and three OFs are employed to see how the OFs affect the positioning and dimensions of FACTS devices. The OF is suggested to consider the reduction of both generation costs and transmission losses while also optimizing the power transfer capacity of designated corridors. A full analysis relating to the IEEE-30 bus system is presented and analyzed.

Intelligent Power System Stability Assessment and Dominant Instability Mode Identification Using Integrated Active Deep Learning.

Simulation analysis is critical for identifying possible hazards and ensuring secure operation of power systems. In practice, large-disturbance rotor angle stability and voltage stability are two frequently intertwined stability problems. Accurately identifying the dominant instability mode (DIM) between them is important for directing power system emergency control action formulation. However, DIM identification has always relied on human expertise. This article proposes an intelligent DIM identification framework that can discriminate among stable status, rotor angle instability, and voltage instability based on active deep learning (ADL). To reduce human expert efforts required to label the DIM dataset when building DL models, a two-stage batch-mode integrated ADL query strategy (preselection and clustering) is designed for the framework. It samples only the most helpful samples to label in each iteration and considers both information contents and diversity in them to improve query efficiency, significantly reducing the required number of labeled samples. Case studies conducted on a benchmark power system (China Electric Power Research Institute (CEPRI) 36-bus system) and a practical large-area power system (Northeast China Power System) reveal that the proposed approach outperforms conventional methods in terms of accuracy, label efficiency, scalability, and adaptability to operational variability.

Power System Stability Improvement Using STATCOM: New Practical Method for Placement and Sizing

Power System Stability Improvement Using STATCOM: New Practical Method for Placement and Sizing

A Contoured Controller Bode-Based Iterative Tuning Method for Multi-Band Power System Stabilizers

An iterative tuning method for multi-band power system stabilizers is proposed, which utilizes the contoured controller Bode (CCBode) plot. The typical multi-band power system stabilizer, PSS4B, is conceptualized as a series connection of two filters: a band-pass filter and a phase compensator. The tuning process involves a space searching approach for the phase compensator to ensure its phase–frequency response remains within acceptable bounds. Subsequently, the CCBode plot is employed to adjust the magnitude–frequency response of the band-pass filter, thereby enhancing stability performance across a broad frequency range. The method proposed can be applied to the parameter design of the multi-band power system stabilizer PSS4B in the power system to suppress the low-frequency oscillations of the local mode and inter-regional mode in the system. The effectiveness of this proposed method is demonstrated through case studies of the four -machine/two-area system and the North China Power Grid.

Power system instability prediction from the solution pattern of differential Riccati equations

AbstractPower system stability characteristics are typically evaluated in terms of small‐ and large‐signal (transient) stability. Access to the time‐varying A‐matrix of a state‐space‐based power systems model during transient conditions can be utilized to apply linear time‐varying system concepts for large‐signal stability analysis. In linear time‐varying system analysis, the differential Riccati equation (DRE) plays a vital role when the power system is subjected to a severe disturbance. The Möbius transformation is proposed in this paper to solve the DRE with singularity issues. It is shown that the solution of the DREs follows a specific mathematical pattern when the power system is stable but does not follow this pattern when the system progresses toward instability. The proposed method can be used in large‐signal stability analysis to predict instability and make the stability analysis more efficient. Additionally, the vector‐DRE is proposed to generalize the index in a large‐scale power system. Results show that analyzing the corresponding Riccati equation's behaviour can help researchers predict a power system's performance and improve the control and management of the system.

Artificial intelligence in pharmacy an overview

Intelligent technologies will someday replace or enhance human talent in many areas. Artificial intelligence is the intelligence exhibited by robots or software. It falls under the field of computer science. Computer scientists are becoming more and more interested in studying artificial intelligence because it has significantly enhanced human lives in a variety of ways. Over the past 20 years, artificial intelligence has led to a major boost in the performance of industrial and service systems. Expert systems are a rapidly developing technology that are the result of research into artificial intelligence. Artificial intelligence applications known as expert systems are widely used today to solve complex problems in a range of fields, including science, engineering, business, medicine, and weather forecasting. Artificial intelligence technology have led to improvements in quality and efficiency in certain sectors. This page gives a summary of the technology and some possible uses for it. This paper will also look at the current applications of artificial intelligence technologies in the fields of computer games, accounting databases, hospital inpatient care, medical image classification, network intrusion, Power System Stabilization (PSS) design, and network security against hackers.