Voltage Reactive Power Research Articles

Coordinated Control of Transient Voltage Support in Doubly Fed Induction Generators

The large-scale integration of wind power significantly alters the voltage dynamic characteristics of power systems. Wind turbines have a weak ability to withstand grid disturbances and have difficulty in providing effective reactive power support during transient periods. The sensitivity of wind turbines to the grid voltage significantly increases the probability of large-scale, cascading off-grid events. This paper proposes a coordinated control strategy to enhance the transient reactive power support capability of doubly fed wind farms. The additional stator current demagnetization control reduces the risk of a crowbar protection action after a fault and ensures that the unit power is controllable. Based on the voltage–reactive power coupling relationship, each unit can produce reactive power according to the voltage–reactive power sensitivity matrix during the transient period. After the reactive power output of the unit reaches the limit, transient active and reactive combined control is further adopted to reduce the active power output of the unit to a certain extent and improve the reactive power support capability. Finally, two cases are built in the PSCAD to verify the effectiveness of the proposed control strategy. The results show that the proposed control strategy can enable the wind farm to output more reactive power to the grid during the transient period, effectively supporting the system voltage during the transient process and avoiding further deterioration of the fault.

Collaborative Optimization for Active Distribution Network with Soft Open Point and Energy Storage

Background: The increasing use of high-penetration distributed clean energy has led to voltage fluctuations in the distribution network that surpass safety limits and pose challenges to economic and low-carbon operation. Traditional voltage regulation devices are unable to meet the real-time high-precision voltage control needs when encountering frequent fluctuations due to physical limitations. The Soft Open Point (SOP) can provide continuous reactive power to achieve rapid voltage regulation. Objective: We propose a collaborative optimization approach that fully considers the regulatory capabilities of SOP and energy storage to eliminate voltage violations and reduce network losses. Methods: This study introduces an active distribution network reactive power voltage optimization approach that incorporates intelligent SOP and energy storage, combining conventional devices (such as on-load tap changers and capacitor banks) with contemporary devices (such as SOP, distributed generators, and energy storage) to establish synergy. A coordinated optimization model was developed, considering various operational constraints to minimize network losses and regulate the voltage of distribution network nodes. By using linearization and quadratic relaxation, the original non-convex mixed-integer non-linear optimization model was transformed into a Mixed-Integer Second-Order Cone Programming (MISOCP) model to meet the requirements of rapid voltage regulation. Results: A case study was conducted on the IEEE 33-node test system, showing that the proposed approach effectively reduces network losses and eliminates voltage violations. Conclusion: The feasibility and effectiveness of the proposed methodology have been validated, confirming its ability to ensure the safe and economic operation of active distribution networks.

New Approaches in Finite Control Set Model Predictive Control for Grid-Connected Photovoltaic Inverters: State of the Art

Grid-connected PV inverters require sophisticated control procedures for smooth integration with the modern electrical grid. The ability of FCS-MPC to manage the discrete character of power electronic devices is highly acknowledged, since it enables direct manipulation of switching states without requiring modulation techniques. This review discusses the latest approaches in FCS-MPC methods for PV-based grid-connected inverter systems. It also classifies these methods according to control objectives, such as active and reactive power control, harmonic suppression, and voltage regulation. The application of FCS-MPC particularly emphasizing its benefits, including quick response times, resistance to changes in parameters, and the capacity to manage restrictions and nonlinearities in the system without the requirement for modulators, has been investigated in this review. Recent developments in robust and adaptive MPC strategies, which enhance system performance despite distorted grid settings and parametric uncertainties, are emphasized. This analysis classifies FCS-MPC techniques based on their control goals, optimal parameters and cost function, this paper also identifies drawbacks in these existing control methods and provide recommendation for future research in FCS-MPC for grid-connected PV-inverter systems.

Network reconfiguration and integration of distributed energy resources in distribution network by novel optimization techniques

Nowadays, electrical load demand in radial distribution networks (RDN) is continuously growing, therefore RDNs are facing some serious challenges like voltage violation and line losses. Since modern RDNs have reconfiguration capabilities, optimal network reconfiguration is preferred over the costly construction of new lines and cables. In addition, the optimal integration of distributed energy resources (DER) helps to decrease above mentioned network challenges. This paper presents an improved version of the radiality maintenance algorithm (IRMA) to solve the optimal reconfiguration problem using a meta-heuristics algorithm. It also proposes two different schemes of algorithms by the blending of a Genetic algorithm (GA) and Teaching learning-based optimization (TLBO) to solve the optimal DER integration problem, where blending enhances the search space of each scheme which helps to find global minima in less iterations and time, while existing algorithms get stuck in local minima with same number of searching agents. The proposed problems are solved by minimizing active power loss by the single objective function and the sum of active power loss, reactive power loss, and voltage deviation index by multi-objective function using the weighted sum method where all objectives are equally dealt with, and their sum is used as single fitness function for the optimization algorithm. Four case studies are simulated using different DER technologies to improve each objective function. The efficiency of the proposed IRMA and two newly developed schemes of optimization algorithms, named GA-TLBO and TLBO-GA, are tested on two IEEE benchmark RDN of 33 and 69 buses. The results validate the efficiency of proposed algorithms that remarkably minimize a single objective from 210.9800 kW in the base case to 58.8768 kW, whereas existing algorithms like GWO and HHO were able to only reduce it to 72.7861 kW and 72.900 kW for IEEE 33 bus RDN, respectively. Similarly, for IEEE 69 bus system, single objective is reduced from base case of 224.9917 kW to 36.2543 kW, while existing algorithms like QOTLBO and QOSIMBO were only able to reduce it to 71.6250 kW and 71 kW, respectively. Furthermore, in multi-objective functions, reactive power losses and voltage deviation are also significantly improved from their base values. After that, the results of improved algorithms are compared with those of existing algorithms in the literature review for a comprehensive evaluation, which proves that proposed algorithm schemes are much more efficient and stable for the proposed problem as well as for standard benchmark optimization functions.

Research on reactive power voltage control strategy of distribution network based on information sharing technology

Abstract In view of the fact that the traditional voltage control of the distribution network is suitable for local voltage control and affects the economy of the operation of the distribution network, a reactive power voltage control strategy of the distribution network based on information-sharing technology is proposed. First, the overall architecture of voltage control in the distribution network based on information-sharing technology is analyzed, and secondly, an algorithm based on the Jacobian sensitivity matrix is established with the minimum voltage RMS as the control goal. The model is solved, and finally, the effectiveness of the proposed voltage control strategy is verified by combining actual examples. The results show that the proposed voltage control strategy has a better voltage regulation effect and effectively reduces the overall voltage deviation of the distribution network.

Research on reactive power voltage control strategy of distribution networks containing distributed PV

Abstract With the large-scale increase in photovoltaic power generation, its integration into the grid will affect the system voltage of the distribution network. This paper presents a reactive voltage control strategy for the distribution network based on deep reinforcement learning. Firstly, the objective function model is established based on the node voltage and network loss of the distribution network. Secondly, each photovoltaic inverter is modeled as an agent of the enhanced learning environment. The collaborative control problem of the photovoltaic inverter is transformed into an observable Markov decision-making process through the distribution network partition. The paper uses a double delay depth deterministic gradient algorithm to solve the problem. Finally, combined with the improvement of 33 nodes for verification, the proposed strategy has the stability of improving voltage, presenting an ability to effectively reduce network loss and verify the effectiveness of the proposed strategy.

Squirrel cage induction generator based micro grid voltage assessment with STATCOM using different metaheuristic approaches

Squirrel Cage Induction Generators are the most common machine to operate wind-based power plants. Microgrids are small-scale electrical systems that provide power to a local area, and optimization methods for microgrids aim to ensure that these systems operate in a way that is both efficient and cost-effective. In such applications, STATCOM is used as reactive power provider and It is necessary to use advanced controllers like the Genetic Algorithm (GA), Firefly Algorithm (FA), and Adaptive Neuro Fuzzy Inference System (ANFIS) as the usual approaches for setting gain factors of STATCOM controller which does not perform well in the case of sudden voltage level changes due to disturbances. The firefly algorithm is primarily required to estimate the gain factors of the STATCOM controller which is used in hybrid micro grid with SG and SCIG for controlling the output voltage in the occurrence of large probabilistic ambiguity. The effectiveness of firefly controlled STATCOM controller for voltage-reactive power control in hybrid microgrid is further examined and validated by comparing the results with other GA, ANN and ANFIS tuning techniques. The need for reactive power is accomplished by STATCOM to minimize the time required to control voltage transient response. The main contribution of this paper includes: an assessment of STATCOM performance with step variations in the input wind energy and reactive power load requirement under conditions of severe probabilistic uncertainty, system analyses conducted under dynamic conditions rather than using a composite load model. Additionally, a comparison of reactive power and voltage control by STATCOM is presented, and employing FA techniques to improve, a comparative of STATCOM reactive power and voltage control. Results comparing all tuning techniques shows that utilizing the Integral of Square of Errors (ISE) criterion, sophisticated tuning techniques can maintain excellent performances under a variety of disruptions.

A detection based on particle filtering and multivariate time-series anomaly detection via graph attention network for automatic voltage control attack in smart grid

The Automatic Voltage Control (AVC) attack is a novel attack that targets voltage control instructions sent to generators from the dispatching center. A successful AVC attack can manipulate reactive power or terminal voltage of generators without being detected, causing the voltages of pilot buses to deviate from the reference values received from the dispatching center. This poses a threat to the safe and stable operation of power systems. This paper proposed a detection based on Particle Filtering (PF) and multivariate time-series anomaly detection via graph attention network (MTAD-GAT). Although each method can detect AVC attacks independently, the coordination of the two methods can be more effective. PF and MTAD are utilized to predict the voltage changes of the pilot bus in the next moment. To combine them, adaptive weights are employed, and an adaptive hybrid prediction can be calculated. The moment can be identified as attacked if the absolute value of the difference between the pilot bus voltage and the reference value exceeds a threshold automatically chosen by Peaks Over Thresholds (POT) theory. The proposed method has been validated through simulations on the IEEE 39-bus 6-partition Coordinated Secondary Voltage Control (CSVC) system and has shown to be effective.

Performance assessment of VSC-based HVDC system in asynchronous grid interconnection: Offline and real-time validation of control design with symmetric optimum PI tuning

Asynchronous interconnection is essential for integrating AC networks operating at different frequencies, typically 50 Hz and 60 Hz. This need arises from distributed power generation methods, including offshore renewable sources and diverse regional grid configurations. Advanced strategies are required to overcome these frequency differences and ensure uninterrupted power transfer.High-Voltage Direct Current (HVDC) transmission systems facilitate efficient power exchange, enhancing grid reliability and stability. This study focuses on optimizing the Proportional-plus-Integral (PI) controller parameters within a 20 MVA Voltage Source Converters (VSC)-based HVDC system to enable asynchronous interconnection between offshore and onshore AC networks. The offshore VSC regulates active and reactive power, while the onshore VSC controls DC voltage and reactive power. A vector control approach with symmetric optimum PI tuning is proposed for a comprehensive performance assessment of the VSC-based HVDC transmission system.The effectiveness of the tuned PI controller parameters is evaluated through four test cases using MATLAB/Simulink for offline simulation and Typhoon HIL604 for real-time validation. These cases involve abrupt changes in reference active and reactive power for the offshore VSC; and in reference reactive power and DC voltage for the onshore VSC. Results demonstrate rapid and satisfactory dynamic performance across all test cases, as evidenced by offline simulations and real-time validation.The validation highlights the effectiveness of the proposed control design with symmetric optimum PI tuning, confirming its ability to enhance the overall performance of the HVDC transmission system for efficient asynchronous interconnection.

Comparative Analysis of Capacitors and Static Var Compensators for Reactive Power Compensation and Voltage Stability in Electrical Grids

Comparative Analysis of Capacitors and Static Var Compensators for Reactive Power Compensation and Voltage Stability in Electrical Grids

Fast excitation structure for improving the transient time of magnetic controlled reactors

With the increasing proportion of renewable energy usage, the randomness and volatility of renewable energy have led to a corresponding increase in the demand for reactive power regulation in the power system. Magnetic controlled reactor (MCR) is one of the technical and economic means used to solve problems such as reactive power compensation and voltage regulation in the power system. However, the transient time of traditional MCR is relatively long compared to other reactive power compensation devices, which limits the further application of MCR. At present, most methods for optimizing the dynamic response time of MCR are to add additional circuits or change control strategies, which will increase the difficulty of MCR control and reduce stability. Therefore, without adding additional circuit structure and control difficulty, this paper proposes a three-phase MCR fast excitation structure that changes the winding connection method, which can improve the DC voltage in the control circuit and reduce the transient time of the MCR. A magnetic circuit model in the MCR core under the fast excitation structure is built and analyzed, based on which, the transient time of the MCR is mathematically calculated. A fast excitation MCR simulation model was constructed based on MATLAB/Simulink platform for dynamic response test simulation. Compared with typical MCR simulation results, the transient time was reduced from 400 ms to 37.1 ms. Dynamic response tests were conducted on a three-phase MCR prototype of 35 kV/18 Mvar, and drop test indicates that the fast excitation structure could reduce the transient time of the three-phase MCR prototype to 36.6 ms, which substantiate the precision of mathematical and simulation results and the efficacy of the fast excitation structure.

Volt-VAR Control in Active Distribution Networks Using Multi-Agent Reinforcement Learning

With the advancement of power systems, the integration of a substantial portion of renewable energy often leads to frequent voltage surges and increased fluctuations in distribution networks (DNs), significantly affecting the safety of DNs. Active distribution networks (ADNs) can address voltage issues arising from a high proportion of renewable energy by regulating distributed controllable resources. However, the conventional mathematical optimization-based approach to voltage reactive power control has certain limitations. It heavily depends on precise DN parameters, and its online implementation requires iterative solutions, resulting in prolonged computation time. In this study, we propose a Volt-VAR control (VVC) framework in ADNs based on multi-agent reinforcement learning (MARL). To simplify the control of photovoltaic (PV) inverters, the ADNs are initially divided into several distributed autonomous sub-networks based on the electrical distance of reactive voltage sensitivity. Subsequently, the Multi-Agent Soft Actor-Critic (MASAC) algorithm is employed to address the partitioned cooperative voltage control problem. During online deployment, the agents execute distributed cooperative control based on local observations. Comparative tests involving various methods are conducted on IEEE 33-bus and IEEE 141-bus medium-voltage DNs. The results demonstrate the effectiveness and versatility of this method in managing voltage fluctuations and mitigating reactive power loss.

Power quality improvement in distribution generation with renewable energy sources using hybrid GBDT-BCMO approach

ABSTRACT This paper presents a hybrid control system designed to improve power quality in grid-integrated hybrid renewable energy sources (HRES). The proposed hybrid control scheme is a hybrid approach gradient boosting decision tree and balancing composite motion optimisation, named GBDT-BCMO. The goals outlined above are the focus of GBDT-BCMO. The GBDT machine learning technique is enhanced using the BCMO method. GBDT method is trained on inputs like previous instantaneous energy of obtainable sources and current time required load demand based on target reference power. Based on load variation, GBDT-BCMO control system creates PI controller gain parameters to generate optimal control signal and manage HRES energy. The prediction process of the present method undertakes variations on system parameters, like reactive and real power and DC voltage. The proposed GBDT-BCMO system improves the power system damping and creates line voltage, giving reactive power compensation. The proposed methodology is then carried out in MATLAB/Simulink platform, and the performance is analysed. The behaviour of the proposed system is related with other systems. The proposed method ranges from 0 to 22.1 V. The BCMO system ranges from 0 to 22. The ALO system ranges from 0 to 22 V. The BAT Algorithm ranges from 0 to 22.0 V.

Optimal allocation of shunt capacitors in radial distribution networks using Constriction-Factor Particle Swarm Optimization and its techno-economic analysis

The optimal penetration of a Shunt Capacitor (SC) is one of the most economical means to enhance the efficiency of radial distribution networks (RDNs). This enhancement includes reducing power loss, and operating costs, improving voltage profiles, and enhancing stability. This paper introduces a constriction-factor Particle Swarm Optimization (Cf-PSO) for SC allocation problem. It leverages the equivalent reactive power voltage stability index (EQVSI) to narrow the search space of the initial Cf-PSO population. The proposed approach analyzes the optimal SC allocation for various scenarios: the base case before SC integration, one SC, two SCs, three SCs, and four SCs. The Cf-PSO technique was tested on the standard IEEE 33- and 69-bus RDNs. For these networks, allocating four SCs resulted in the most significant percentage reduction in power loss: 35.15 % for the IEEE 33-bus and 35.85 % for the IEEE 69-bus, compared to their respective base scenarios. The simulation results also demonstrate that as more SCs are optimally installed, the percentage reduction in power loss and the financial cost-benefit improve. However, beyond two SCs, the rate of improvement significantly diminishes, reaching a point where adding more SCs is no longer economically advantageous compared to the previous number. The power loss results obtained using this approach were validated by comparing them to established techniques found in the open literature, confirming their effectiveness in most cases.

Distributed Voltage Reactive Power Control of Distribution Network Considering the Coordination Between DPV and SVC

With the increasing permeability rate of DPV in the distribution network, the safe and economic operation of the distribution network is faced with many challenges, among which the problem of voltage exceeding the limit must be solved urgently. A method for distributed voltage reactive power control, which involves coordinating DPV and SVC, is suggested to enhance the voltage regulation capacity in high-permeability distribution networks. Firstly, considering the limited reactive power regulation capacity of DPV, a coordinated control strategy is proposed to enable SVC when the reactive power capacity of DPV is insufficient. Secondly, a voltage-reactive power control model for distribution networks is established, with a focus on minimizing node voltage deviations and network losses. An adaptive penalty factor adjustment strategy, based on the alternating direction multiplier method, is introduced to achieve a comprehensive solution for this model. Finally, the enhanced IEEE 33-node system is utilized for verification and analysis. The outcomes demonstrate that the suggested coordination strategy involving DPV and SVC, alongside the distributed optimization control method, offers economic advantages and effectively achieves the safe and cost-effective operation of the distribution network.

A flexible energy management for distributed energy supplied smart home in microgrid connection with grid‐connection/islanding mode functionality

AbstractIn this study, a flexible control approach is presented for smart home with DES system in microgrid connection. The smart home with DES has operation capability under GC‐Mode and I‐Mode functions with the proposed control algorithm. The control algorithm is also developed in terms of international standards IEEE 929 and IEC 61727 for grid connection requirements. Additionally, a PLL‐less control algorithm is embedded in the proposed control algorithm for the active/reactive power management and the grid synchronization. Thus, slow dynamic response and calculation burden issues in controller is avoided and the power flow capability of the grid‐connected DES in smart home is enhanced. Furthermore, the coordinate transformation is fulfilled for the power flow control in the control loop via the generation of cosine and sinus functions. Besides, the mathematical formulations of the proposed control algorithm are presented in detail. To show the effectiveness of the proposed control algorithm, a smart home with a photovoltaic DES model is designed and constructed in a simulation environment. The proposed control method is tested under various operation cases such as dynamic environmental conditions, GC‐Mode/I‐Mode, reactive power support and voltage variations. The proposed method shows efficient performance under applications of the different operation situations. Moreover, the international requirements are satisfied for GC‐Mode and I‐Mode transitions.

Allocation and Sizing of DSTATCOM with Renewable Energy Systems and Load Uncertainty Using Enhanced Gray Wolf Optimization

Over the last decade, flexible alternating current transmission systems (FACTS) have been crucial in ensuring optimal power distribution within modern power systems. A vital component of FACTS devices is the distribution static compensator (DSTATCOM), which is essential for maintaining a reliable power supply. It is commonly used for reactive power compensation, voltage regulation, and harmonic reduction. Determining the appropriate size and placement of DSTATCOMs is vital to ensuring their efficiency. This study introduces the improved gray wolf optimizer (I-GWO), a refined version of the classical gray wolf optimization (GWO) method. The I-GWO incorporates a dimension learning-based hunting (DLH) strategy to preserve population diversity, balance exploration and exploitation, and prevent the premature convergence of classical GWO. In this research, the I-GWO was applied to determine the optimum allocation and sizing of the DSTATCOMs, considering system constraints, including those presented by the intermittent and stochastic nature of the load and renewable energy resources, specifically wind and solar energy. The suggested approach was successfully tested on 33-, 69-, and 85-bus distribution systems and then compared with existing studies. The results demonstrated the I-GWO-based approach’s superiority in terms of reducing power losses, improving voltage profiles, and enhancing voltage stability.

A SOCP-Based ACOPF for Operational Scheduling of Three-Phase Unbalanced Distribution Systems and Coordination of PV Smart Inverters

The proliferation of distributed energy resources (DERs) imposes new challenges to distribution system operation, e.g., power quality issues. To overcome these challenges and enhance system operation, it is critical to effectively utilize all available resources and accurately characterize unbalanced distribution networks in operational tools. This paper proposes a convex second-order-cone programming (SOCP)-based AC optimal power flow (ACOPF) model for three-phase unbalanced distribution networks, including smart inverters and Volt-VAr controller (VVC) devices. Reactive power-voltage (Q-V) characteristics of smart inverters of solar photovoltaic (PV) units are also modeled. Moreover, the settings of Q-V characteristics of VVC are co-optimized within the proposed ACOPF, considering the allowable range of the IEEE 1547-2018 standard. Furthermore, dynamic analyses are conducted to verify the stability of optimal settings of VVC. The proposed models are tested on an actual 1747-node primary distribution feeder in Arizona. The results illustrate the effectiveness of the proposed ACOPF for unbalanced systems in providing an optimal solution while capturing the non-linearity and non-convexity of ACOPF. By co-optimizing settings, system operation is improved due to the flexibility of adjusting reactive power output from PV units with VVC. The time-domain simulations show that the optimal settings cause no stability issue for the distribution system.

Real-time Neural Sliding Mode Linearization Control for a Doubly Fed Induction Generator under Disturbances

Abstract This paper presents an experimental implementation of a Neural Sliding Mode Linearization approach for the control of a double-fed induction generator connected to an infinite bus via transmission lines. The rotor windings are connected to the grid via a back-to-back converter, while the stator windings are directly coupled to the network. The chosen control scheme is applied to obtain the required stator power trajectories by controlling the rotor currents and to track the desired values of the DC-link output voltage and the grid power factor. This controller is based on a neural identifier trained online using an Extended Kalman Filter. Based on such identifier, an adequate model is obtained, which is used for synthesizing the required controllers. The proposed control scheme is experimentally verified on 1/4 HP DFIG prototype considering normal and abnormal grid conditions. In addition, maximum power extraction from a random wind profile is tested in the presence of different grid scenarios. Moreover, a comparison with conventional control schemes is performed. The obtained results illustrate the capability of the proposed control scheme to achieve active power, reactive power, and DC voltage desired trajectories tracking and to operate the wind power system even in the presence of parameter variation and grid disturbances, which helps to ensure the stability of the system and improve generated power quality.

Voltage control method for multi-energy system based on the coupling of renewable energy hydrogen production and storage

Renewable energy power generation combined with hydrogen storage, is an important way to realize the deep integration of hydrogen energy and renewable energy in multi-energy systems. In the multi-energy system of renewable energy power generation and hydrogen production in AC/DC hybrid. Due to the fluctuation of heat load and electric hydrogen production load in the system and the multi-time scale characteristics of energy flow transmission of heat storage and hydrogen storage. It will lead to the difficulty of power coordination in the whole Multi Energy System (MES). In order to solve the problem of power imbalance in MES, this paper first establishes the hybrid power flow equation of MES cluster composed of AC/DC and hydrogen energy transmission channels. The reactive voltage fluctuation characteristics of the AC/DC system under the fluctuation of energy exchange, system energy conversion, and renewable energy output of the MES cluster interconnection channel are analyzed. Secondly, a multi-energy flow model of the multi-energy system is established. The main factors affecting the voltage-reactive power of the AC system are analyzed. A reactive power model based on electro-thermal conversion, hydrogen storage, and multi-energy transmission channels is established. Then, a generation algorithm of the safe and stable boundary of pipeline pressure is proposed to maintain the pressure of hydrogen production and storage system within a safe range. The robust optimization control method is used to control the coupling of multi-energy sources. Finally, based on the actual operation data of the multi-energy power system. The simulation model of reactive power dynamic optimization control for multi-energy interconnected systems is established. The simulation results show that the control method proposed in this paper is adopted. Through the coordinated distribution of energy among multi-energy systems, the voltage stability level of the system can be effectively improved.