IEEE-30 Bus Research Articles

Adaptive Step Size Linear Multistep Method for Small Signal Stability Analysis in Power Systems with Time Delays

Distributed energy resources (DERs) offer a promising solution for enhancing the resilience and efficiency of modern power systems. However, the inherent time delays in their communication and control systems can degrade their small signal stability, affecting reliable operation. This paper presents an Adaptive Step Size Linear Multistep Method (AS-LMS), for analyzing small signal stability of the system in the presence of time delays. The AS-LMS automatically adjusts the step size based on the computed solution behavior, achieving a balance between accuracy and computational efficiency. The essential idea is to use smaller steps in regions where the dynamic is changing rapidly and larger steps in regions where it is relatively smooth. Two adaptive step size strategies are proposed. The first strategy compares the current step size to the initial step size, ensuring consistency across iterations. The second strategy compares the current step size with the previous step size, allowing for more local adjustments throughout the numerical integration process. We evaluate the performance of the proposed AS-LMS method on two standard testing systems, i.e., the IEEE 9-bus and IEEE 30-bus systems. Both systems are modified to converter-interfaced DER units and different delays are applied to each testing system. The results show that the proposed AS-LMS method improves computational efficiency while maintaining accuracy in stability assessments. By balancing the accuracy and computational efficiency, the AS-LMS method provides a potent tool to analyze the stability of power systems with time delay.

Stability Analysis of Optimized PMU Placement using Hybrid and Individual TLBO-PSO Techniques

In power system to optimized PMUs is a critical task to ensure maximum network observability while minimizing installation costs. This study presents a comparative analysis of three optimization techniques: Teaching-Learning-Based Optimization (TLBO), Particle Swarm Optimization (PSO), and a hybrid TLBO-PSO approach, focusing on their efficiency in determining the best PMU placements. Individual methods, such as TLBO and PSO, are often limited by longer computation times and the requirement for a higher number of PMUs to achieve full observability. In contrast, the hybrid TLBO-PSO method demonstrates significant improvements, consistently delivering solutions with fewer PMUs, faster computation times, and higher placement accuracy. By evaluating performance of these techniques on IEEE 14bus, 30bus and 57 bus systems through simulations conducted over 100 iterations for each method in every test case. The results highlight the hybrid approach's superior efficiency compared to individual methods. Furthermore, comparisons with prior research confirm that the hybrid TLBO-PSO approach is a robust and reliable solution for minimizing PMU installations while ensuring complete system observability.

Vulnerability analysis on random matrix theory for power grid with flexible impact loads

The stochastic volatility of the rail transit load brings greater uncertainty to the vulnerability of the power grid. To solve the problem of the inaccurate results caused by the incomplete time-domain simulation model of the power system with rail transit load integration, this paper proposes a vulnerability analysis method for the power system with rail transit load integration based on the random matrix theory. In this paper, we first constructed a rail transit load model based on Deep Convolutional Generative Adversarial Networks (DCGAN) to simulate the situation that massive rail transit load merged into the Grid Scenario. Then, we generate a high-dimensional random matrix based on the power flow of the grid-connected system under different rail transit loads. Then, we construct a vulnerability analysis model combining the random matrix theory and the real-time separation window. Finally, we take the IEEE-39 bus system and a regional power grid in China as examples to evaluate the vulnerability of the grid-connected system. The results show that our method quantifies not only the impact of the rail transit load volatility on the system vulnerability, but the system endurance under different capacities of the rail transit load connected to grid. Moreover, it also provides a new way for system planning and safety monitoring in the power system with rail transit load integration.

An artificial insurance framework for a hydrogen-based microgrid to detect the advanced cyberattack model

Microgrid systems have evolved based on renewable energies including wind, solar, and hydrogen to make the satisfaction of loads far from the main grid more flexible and controllable using both island- and grid-connected modes. Albeit microgrids can gain beneficial results in cost and energy schedules once operating in grid-connected mode, such systems are vulnerable to malicious attacks from the viewpoint of cybersecurity. With this in mind, this paper explores a novel advanced attack model named the false transferred data injection (FTDI) attack aiming to manipulatively alter the power flowing from the microgrid to the upstream grid to raise voltage usability probability. One crucial piece of information that the model uses to change the system and cause the greatest amount of damage while concealing the attacker’s view is the voltage stability index. Saying that the power transaction between the microgrid and the upstream grid is within the broad scope of bilateral exchange at any given moment is noteworthy. Put otherwise, with respect to the FTDI assault, the microgrid’s power direction is just as significant to the detection system as the transferred power value. Therefore, once the microgrid is running in the grid-connected mode, the false data detector needs to concurrently detect changes in the value and direction of power. To overcome this problem, the paper presents a learning generative network model, based on the generative adversarial network (GAN) paradigm, to recognize the change in probability values that is maliciously aimed. To this end, a studied microgrid system including the wind turbine, photovoltaic, storage, tidal turbine, and fuel cell units is performed on the tested 24-bus IEEE grid to satisfy the local load demands. Comparative analysis indicates notable gains, such as scores of 0.95%, 0.92%, 0.7%, and 10% for the Hit rate, C.R. rate, F.A. rate, and Miss rate in order to evaluate the GAN-based detection model within the microgrid.

New Energy Access Distribution Network Line Loss Management Based on Deep Learning and Fuzzy Algorithm

This paper introduces a novel method for managing line losses in energy access distribution networks, utilizing deep learning and fuzzy algorithms. As new energy sources become more integrated into distribution networks, traditional line loss management techniques struggle to address the complexities and uncertainties that arise. To tackle this issue, a comprehensive model for line loss management is developed, leveraging the predictive strengths of deep learning alongside the fuzzy algorithm’s capability to handle uncertainty. The deep learning model forecasts the operational conditions of the power grid following the integration of new energy. At the same time, the fuzzy algorithm assesses and adjusts potential line losses, facilitating effective management of distribution network line losses. The model is trained and validated using a comprehensive dataset from the IEEE 39-Bus System, with performance metrics like Accuracy, F1-score, and recall rate evaluated against traditional models like CNN and GAT. The results show that the FGAN model achieves an accuracy of 96.32%, an F1-score of 95.17%, and a recall rate of 97.05%, significantly outperforming baseline models. Experimental findings indicate that this proposed method substantially reduces line losses and improves the efficiency and reliability of the distribution network. This research offers a fresh perspective on line loss management for intelligent distribution networks in the context of new energy integration, demonstrating considerable practical value.

Comparative analysis of FACT devices for optimal improvement of power quality in unbalanced distribution systems

This study examines the performance of asymmetric three-phase distribution systems under the influence of FACT deives such as a static VAR compensator (SVC) and a unified power controller (UPC). Each suggested device’s operating principle is developed in this paper in order to provide the best model to be used in the power flow analysis. The performance of the IEEE-13 bus imbalanced distribution model is investigated using the Newton-Raphson method. To improve network asymmetry, an optimization analysis is carried out to ascertain how each proposed device will work. In this paper, the mode of operation of each device is determined every hour to flow the changing in loads according to their daily load curve. The genetic algorithm (GA) is utilized to determine the contribution of the unified flow controller to balance the loads between phases. The multi-objective function is constructed to combine the total system losses and the average voltage unbalance coefficient. The paper provides an optimal dynamic technique to optimally operate the unified power flow controller installed in the unbalanced three-phase distribution system.

On-line strength assessment of distribution systems with distributed energy resources

To enable the online strength assessment of distribution systems integrated with Distributed Energy Resources (DERs), a novel hybrid model and data-driven approach is proposed. Based on the IEC-60909 standard, a new short-circuit calculation method is developed, allowing inverter-based DERs (IBDERs) to be represented as either voltage or current sources with controllable internal impedance. This method also accounts for the impact of distant generators by introducing a site-dependent Short Circuit Ratio (SCR) index to evaluate system strength. An adaptive sampling strategy is employed to generate synthetic data for real-time assessment. To predict the strength of distribution systems under various conditions, a rectified linear unit (ReLU) neural network is trained and further reformulated as a mixed-integer linear programming (MILP) problem to verify its robustness and input stability. The proposed method is validated through case studies on modified IEEE-33 and IEEE-69 bus systems, demonstrating its effectiveness regarding the varying operating conditions within the system.

Explainable Warm-Start Point Learning for AC Optimal Power Flow Using a Novel Hybrid Stacked Ensemble Method

With the development of renewable energy, renewable power generation has become an increasingly important component of the power system. However, it also introduces uncertainty into the analysis of the power system. Therefore, to accelerate the solution of the OPF problem, this paper proposes a novel Hybrid Stacked Ensemble Method (HSEM), which incorporates explainable warm-start point learning for AC optimal power flow. The HSEM integrates conventional machine learning techniques, including regression trees and random forests, with gradient boosting trees. This combination leverages the individual strengths of each algorithm, thereby enhancing the overall generalization capabilities of the model in addressing AC-OPF problems and improving its interpretability. Experimental results indicate that the HSEM model achieves superior accuracy in AC-OPF solutions compared to traditional Deep Neural Network (DNN) approaches. Furthermore, the HSEM demonstrates significant improvements in both the feasibility and constraint satisfaction of control variables. The effectiveness of the proposed HSEM is validated through rigorous testing on the IEEE-30 bus system and the IEEE-118 bus system, demonstrating its ability to provide an explainable warm-start point for solving AC-OPF problems.

Two-stage multi-objective framework for optimal operation of modern distribution network considering demand response program.

To improve the inadequate reliability of the grid that has led to a worsening energy crisis and environmental issues, comprehensive research on new clean renewable energy and efficient, cost-effective, and eco-friendly energy management technologies is essential. This requires the creation of advanced energy management systems to enhance system reliability and optimize efficiency. Demand-side energy management systems are a superior solution for multiple reasons. Firstly, they empower consumers to actively oversee and regulate their energy consumption, resulting in substantial cost savings by minimizing usage during peak hours and enhancing overall efficiency. The Demand Response Program (DRP) and optimal power sharing have gained significant attention to provide technical and economic benefits, while they require an efficient operation framework. Therefore, a two-stage framework is proposed for multi-objective operation of a distribution network with several generation resources. The first stage applies DRP to maximize the distribution network operator's (DNO) profit by optimizing common incentive rate for all consumers participate in DRP and an individual curtailed power for each consumer. In addition to an individual incentive rate for each consumer participates in DRP which is a new solution in the field of demand side management. The second stage achieves optimal power sharing among generation resources, while considering multiple objectives and incorporating the modified load of the first stage. The multi-objective problem is formulated to reduce energy losses, voltage deviation, total operational cost, gas emissions, and maximize the voltage stability index. The problem is optimized using a combination of the technique for order of preference by similarity to ideal solution (TOPSIS) and the elephant herding optimization (EHO) technique. The framework is validated using a modified IEEE 33-bus that incorporates photovoltaic system, diesel generators, and wind generation system. The proposed framework based on an individual incentive rate DRP provides superior response compared to common incentive rate DRP which reduces the total energy losses by 38.13%, reduces the total generation cost by 9.468%, and reduces the emission by 5.9%.

Optimal active and reactive power dispatch in the presence of wind farms using voltage indicators and metaheuristic methods

Wind power plants penetration into power grids has grown considerably due to the fact of being cheap and clean. As a result, many countries require that the wind turbines must be capable of offering ancillary services such as reactive power control and voltage regulation. Furthermore, some voltage stability indices rely on both reactive and active power; consequently, it is useful to take also into account the active power during the planning stage so as to fulfil the load requirement and to enhance the voltage stability. The present study intends to present an optimization algorithm permitting to diminish the electrical losses and enhance the voltage profile by getting the fittest allocation of active power production of traditional generators and wind power plants. Besides, wind farms reactive power injection is determined. To verify the proposed algorithm, a 40 MW wind farm (WF) made up of doubly fed induction generator (DFIG) and the 14 IEEE bus system are used. In the case study, three different metaheuristic methods are used to resolve the objective function. Finally, the simulation results are reported.

Application of the design space approach for optimal sizing and placement of distributed generation

Distributed generation relies on the generation and feeding of electrical power locally into the utility grid, thereby avoiding transmission and distribution losses in centralised generation and distribution. A methodology for the optimum sizing of distributed generators (DG) in a radial grid is proposed in this work. On the basis of distributed load flow analysis, the system performance, along with different constraints viz., the power stability index, branch current and voltage, the complete set of feasible design options, also known as the design space, has been identified. The design space of a distributed energy system identifies the entire solution set of DG rating, power loss and branch current within which a feasible system may be designed. The optimum size and placement of DG in the particular cases of 12-bus, IEEE 13-bus and IEEE 33-bus radial distribution systems are illustrated using the proposed approach. The optimum configuration is identified by minimising the total power losses as the objective function. The proposed approach is quick and gives a range of options rather than a single solution, thereby offering valuable insight into possible alternatives during the early stages of design.

Enhancing power system stability in wind-integrated networks through coordinated fuzzy logic control of PSS and POD for sustainable energy futures

In the face of challenges posed by increasing wind power integration, penetrations, SG replacement with Wind Farm (WF), and significant disturbances and their impact on small signal and transient stability. This paper presents a novel solution employing coordinated control of Power System Stabilisers (PSS) and Power Oscillation Dampers (POD). The stochastic nature of wind power and nonlinear wind speed behaviour creates complexities and is exacerbated by severe disturbances. Traditional PSS and POD designs struggle with these non-linearities. An intelligent coordinated control strategy based on a Fuzzy Logic Controller (FLC) is proposed to address this issue. The FLC synchronises and coordinates PSS and POD, enhancing stability by dampening Low-Frequency Oscillations (LFOs) and mitigating undamped fluctuations even under extreme conditions. Sensitivity analysis identifies key LFOs affecting stability, enabling optimal control gain selection. RT-LAB experimental platform on the IEEE-9 bus system validates the approach, showcasing superior performance compared to individual methods. The results underscore the robustness and reliability of FLC coordination, ensuring enhanced system stability in practical applications.

Dynamic reconfiguration of multiobjective distribution networks considering the variation of load and DG using a novel LDEDBO algorithm

To address the challenges related to active power dissipation and node voltage fluctuation in the practical transformation of power grids in the field of new energy such as wind and photovoltaic power generation, an improved Dung Beetle Optimization Algorithm Based on a Hybrid Strategy of Levy Flight and Differential Evolution (LDEDBO) is proposed. This paper systematically addresses this issue from three aspects: firstly, optimizing the DBO algorithm using Chebyshev chaotic mapping, Levy flight strategy, and differential evolution algorithm; secondly, validating the algorithm’s feasibility through real-time network reconfiguration at random time points within a 24-h period; and finally, applying the LDEDBO to address the dynamic reconfiguration problems of the IEEE-33 and IEEE-69 node bus. The simulation indicates that the power dissipation of the IEEE-33 node bus is decreased by 28.94% and the minimum node voltage is elevated from 0.9273 p.u to 0.9447 p.u after the reconstruction with LDEDBO. The power dissipation of the IEEE-69 node bus is reduced by 36.45%, and the minimum node voltage is increased from 0.9224 p.u to 0.9481 p.u. The LDEDBO enhances both the pace of convergence and the precision of the optimization model, leading to a superior solution for the switching combination.

A novel fuzzy assisted sliding mode control approach for frequency regulation of wind-supported autonomous microgrid

Autonomous microgrids (ATMG), with green power sources, like solar and wind, require an efficient control scheme to secure frequency stability. The weather and locationally dependent behavior of the green power sources impact the system frequency imperfectly. This paper develops an intelligent, i.e., fuzzy logic-based sliding mode control (F-SMC) utilizing a proportional-integral-derivative (PID) type sliding surface to regulate the frequency of a wind-diesel generator-based ATMG system. A dynamic structure of the wind generator is designed to participate in the frequency support of the considered plant. The mastery of the F-SMC is analyzed over the conventional SMC (C-SMC) under load perturbation. This study used the artificial gorilla troop optimization (GTO) technique to tune the F-SMC parameters. The effectiveness of the GTO-tuned F-SMC frequency regulation (FR) scheme is compared with well-established particle swarm optimization (PSO) and grey wolf optimization (GWO) approaches under various scenarios such as load perturbations, governor dead band (GDB), generation rate constraint (GRC), higher/lower dimensions of ATMG, and wind speed variations. Finally, the proposed GTO-based F-SMC approach has been validated upon a standard IEEE-14 bus system and compared with recent techniques.

Optimal Power Flow using An Optimally Tuned Pattern Search Algorithm

Optimal power flow (OPF) is a critical optimization application in power system planning and operation. Numerous studies employ metaheuristic techniques to address OPF problems of varying complexity. However, these techniques often suffer from slow convergence due to their dependence on the quality of initial solutions. To overcome this limitation, initial solutions must be optimally tuned to achieve good outcomes with faster convergence. This paper proposes an optimally tuned pattern search (OPS) algorithm to solve OPF problems in medium and large power systems. The tuning process, performed using the classical interior point method (IPM), provides optimal initial control variable values for the standard pattern search (PS) algorithm. The proposed technique is applied to three test systems: IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus systems. The OPF problem is formulated to minimize four objectives: total active power loss, total generator fuel cost, total generator emission, and total deviation in load bus voltage magnitude. The performance of the OPS algorithm is evaluated based on objective function values and computation times and is compared with IPM and two popular metaheuristic techniques, particle swarm optimization (PSO) and genetic algorithm (GA). Results indicate that the OPS algorithm's performance varies across test systems but generally balances optimization performance with computational efficiency.

Interline Power Flow Controller Allocation for Active Power Losses Enhancement Using Whale Optimization Algorithm

Transmission networks face continuous electrical and mechanical stresses due to increasing system challenges and power losses. Transmission networks require special focus and detailed studies each time a load or a generator emerges to the grid. The interline power flow controller (IPFC) is a relatively new scheme that is implemented in the transmission network to improve transmission efficiency, decrease transmission losses, and enhance voltage profile. In this paper, the interline power flow controller’s impact on transmission network performance is investigated as it is implemented within the IEEE 5-bus, 14-bus, and IEEE 57-bus systems. In addition, the whale optimization algorithm (WOA) is used to optimize the interline power flow controller locations within the system to achieve optimal transmission system performance. WOA performance is also compared to genetic algorithm (GA) and particle swarm optimization (PSO) algorithms, and the superiority of the proposed WOA-based control is proved. The robustness of the optimized system against load variations is investigated and the results introduced affirm the capability of the interline power flow controller to enhance transmission network efficiency.

Attentional Convolutional Neural Network Based on Distinction Enhancement and Information Fusion for FDIA Detection in Power Systems

A false data injection attack (FDIA) is one of the major threats to power systems, and identifying false data is critical to the stable operation of power systems. However, false data that closely resemble normal data hinder the accuracy of existing detection methods, and their performance further declines when exposed to ambient noise. To address these challenges, this paper proposes an attentional convolutional neural network based on distinction enhancement and information fusion (DEIF-ACNN) for FDIA detection. Firstly, by minimizing the loss of reconstruction and discrimination, this paper designed an autoencoder with a discriminator for normal data (NAE), which had the characteristic of producing a small loss for normal data. Secondly, the trained NAE is utilized to compute the feature correlation matrix between the original and reconstructed data to enhance the distinction between normal and false data. Finally, to enhance feature extraction and suppress ambient noise interference in detection, DEIF-ACNN incorporates a convolutional block attention module (CBAM) to emphasize key feature channels and highlight crucial regions in the feature matrix. Experimental results show that DEIF-ACNN outperforms other FDIA detection methods on IEEE-9, IEEE-14, and IEEE-118 bus power systems, achieving an accuracy of 99.22%, 99.83%, and 100.00%, respectively. In addition, the method exhibits the best robustness under different noise environments, and its accuracy is maintained at about 80%.

Distributed secondary frequency control and state of charge (SoC) balance for droop-controlled battery energy storage systems (BESSs) with a unified SoC relative variation rate

The state of charge (SoC) balance, power sharing, and frequency restoration are common control objectives of battery energy storage systems. However, the SoC balance scheme induced by the power allocation through existing droop controllers can cause the capacity parameters of battery cells to be unequal to the droop coefficient, which is the result of battery capacity degradation. Under this limitation, previous capacity-based droop controllers and the secondary controllers are no longer suitable to address the imprecise power sharing and frequency restoration caused by this problem. Therefore, a power allocation scheme based on the current SoC level ratio is designed to induce a new droop controller and ensure that the SoC simultaneously drops to 0. In order to restore frequency in a distributed manner and obtain the SoC level ratio, a distributed nominal frequency controller, SoC average estimator, and power-sharing controller are designed based on multi-agent systems in both asymptotic and finite-time manners. In the asymptotic scheme, the steady-state performance of the SoC estimator is adjustable, and power sharing and frequency restoration are zero errors. In the finite-time scheme, the influence of parameters on convergence time is well analyzed, and some conservative calculation methods are provided. Several time-domain simulation examples are designed on an improved IEEE-57 bus system to verify the distribution of asymptotic and finite-time schemes.

Enhancing power quality in solar-wind grid-connected systems through soft computing techniques

This work intends to improve estimates of solar and wind energy generation through the application of resilient backpropagation control and substantial power evolution strategy (SPES) algorithms. In comparison to particle swarm optimization and genetic algorithms, the main goal is to minimize predicting mistakes. These methods increase grid reliability by lowering total harmonic distortion (THD) and improving power quality when integrated with the IEEE-9 bus standard. In order to evaluate the hybrid system's transient and steady-state reactions, the study also highlights the importance of bolstering operation and control. A revolutionary deep learning-based approach is also suggested for predicting wind and solar hybrid energy. The power grid's efficiency and dependability in handling renewable energy sources have significantly improved, according to the results.

Application of Walrus Optimizer for Power Quality Improvement in Radial Distribution System

To increase power quality (PQ) in radial distribution systems (RDS) by utilizing active power filters (APFs), this research discusses the application of walrus optimization algorithms (WaOA). The main problem with the PQ is harmonics. The harmonics are added to the RDS by nonlinear loads (NLs). In this instance, together with NL at two end nodes, nonlinear distributed generation (NLDG) is additionally considered. APFs are used to decrease the harmonics to specified limits. In this instance, APFs are positioned correctly to reduce harmonics and improve PQ. WaOA is utilized to maximize the APF's size at the ideal bus location. The WaOA is inspired by natural processes and contains features that are well-balanced for both exploration and exploitation. Within limitations on inequality, optimization seeks to minimize APF's current. On the IEEE-69 bus RDS, a simulation is run to assess the WaOA's performance. Four distinct cases are examined here: a) NL+NLDG only (no APF); b) APF at bus 27; c) APF at bus 67, and d) APFs are located at busses 27 and 67. These examples are taken into consideration to examine the impact of APF sizing and location on PQ in RDS. Using the artificial bee colony (ABC) optimization method, a comparison analysis is performed. The simulation results confirm that the WaOA algorithm solves the optimization problem with stability and efficacy.