Voltage Control Method Research Articles

Improved multistep model predictive control method with reduced finite set for MMC-UPQC

Multistep model predictive control (M-MPC) can improve control accuracy but leads to an increased computational burden. To reduce the computational burden and restore power quality, an improved M-MPC method with reduced finite set for unified power quality conditioner (UPQC) based on modular multilevel converter (MMC) is presented in this paper. First, a finite set of M-MPC is formed according to the inserted sub-modules (SMs) number calculated from cost functions. Second, we investigate the relationship between the inserted SMs number and the bridge arm output voltages and find that the inserted SMs number will increase as voltages rise. As such, by judging the change rate of the output voltages, switching states that cannot be realized within the finite set can be eliminated, thus the finite set can be reduced. In addition, circulating currents and capacitance voltage control methods are designed to ensure the internal stability of MMC. Finally, the MATLAB/Simulink simulation and the real time laboratory (RT-LAB) based hardware-in-the-loop (HIL) experimental results show effectiveness of the proposed M-MPC. The results show the proposed M-MPC exhibits better dynamic characteristics and fewer control options as compared to the single-step MPC.

Circulating Current Control in Interleaved and Parallel Connected Power Converters

This article analyzes circulating current control in single-phase power electronic converters, focusing on two different topologies: interleaved and parallel configurations. The study involves a bridgeless interleaving topology with two boost converters for increased efficiency. A parallel connection is also examined for monitoring line current, circulating currents, and power factor control. The article widely explains all current loops, including Common Mode Circulating Currents (CMCC) in the bridgeless interleaved topology and Differential Mode Circulating Currents (DMCC) in parallel-connected interleaved power converters. The proposed control scheme employs voltage and current control loops for output voltage and line current control and introduces CMCC and DMCC compensators to eliminate all types of circulating currents. An efficient Power Factor Correction (PFC) and output voltage control method is presented in this article. The effectiveness of the proposed schemes is validated through comparisons with modern control systems. The results are verified using Simulink/MATLAB and experimental setups with TI Instruments Piccolo prototypes and C2000 (TMS320F28035 microcontroller MCU) microcontrollers in parallel configurations.

Safe and Stable Secondary Voltage Control of Microgrids Based on Explicit Neural Networks

This paper proposes a novel safety-critical secondary voltage control method based on explicit neural networks (NNs) for islanded microgrids (MGs) that can guarantee any state inside the desired safety bound even during the transient. Firstly, an integrator is introduced in the feedback loop to fully eliminate the steady-state error caused by primary control. Then, considering the impact of secondary control on the stability of the whole system, a set of transient stability and safety constraints is developed. In order to achieve online implementation that requires fast computation, an explicit NN-based secondary voltage controller is designed to cast the time-consuming constrained optimization in the offline NN training phase, by leveraging the local Lipschitzness of activation functions. Specially, instead of using the NN as a black box, the explicit representation of NN is substituted into the closed-loop MG for transferring the stability and safety constraints. Finally, the NN is trained by safe imitation learning, where an optimization problem is formulated by maximizing the imitation accuracy and volume of the stable region while satisfying the stability and safety constraints. Thus, the safe and stable region is approximated that any trajectory initiates within will converge to the equilibrium while bounded by safety conditions. The effectiveness of the proposed method is verified on a prototype MG with detailed dynamics.

Optimal Distributed Coordinated Reinforcement Learning for Secondary Voltage Control in Time-Delayed Microgrid

This article proposes an optimal distributed secondary voltage control method based on distributed coordinated reinforcement learning (DCRL) algorithm in the time-delayed microgrid. First, a distributed secondary voltage control protocol through a sparse communication network is designed using consensus-based algorithm and Lyapunov–Krasovskii theory. Then, by combining the consensus control method with reinforcement learning together, a novel DCRL algorithm is utilized to dynamically adjust the voltage controller gains. Since the DCRL method is an online intelligent algorithm, it can adaptively improve the voltage control performance in the presence of various external disturbances encountered in microgrid secondary control. The effectiveness of the proposed method is verified through simulations under different scenarios on a four inverter-based microgrid test system. Simulation results indicate that our method shows superior dynamic voltage regulation response over traditional methods.

Optimal Voltage Control Method for a Step Voltage Regulator Considering the Under-Load Tap Changer in a Distribution System Interconnected with a Renewable Energy Source

The voltage in distribution systems is controlled by the under-load tap changer of the substation and the pole transformer of the primary feeders. Recently, as one of the main countermeasures, a step voltage regulator is being introduced to solve voltage problems such as overvoltage phenomena in a distribution feeder interconnected with a renewable energy source and under voltage in a long-distance feeder. However, the tap of the step voltage regulator may be frequently operated due to its interdependent relationship with the under-load tap changer in the distribution system. Furthermore, given the existing operating characteristics of the step voltage regulator, it is difficult to perfectly maintain the customer voltage within an allowable limit using existing methods such as the line drop compensation method for a step voltage regulator. In addition, the existing line drop compensation method, considering the distributed generators, may be not able to control the proper voltage within an allowable limit. Therefore, in order to solve such voltage problems, this paper proposes a voltage control method for a step voltage regulator by considering the output voltage of an under-load tap changer that is operated via the line drop compensation method. In other words, to overcome the limitations of existing voltage control methods for step voltage regulators, this paper proposes an optimal control method to determine the optimal compensation rate for a step voltage regulator by considering the reverse power flow from a renewable energy source and the output voltage of the under-load tap changer of the main transformer.

A Policy optimization-based Deep Reinforcement Learning method for data-driven output voltage control of grid connected solid oxide fuel cell considering operation constraints

Solid oxide fuel cells (SOFCs) have many applications in microgrids, but they often face the challenge of maintaining the fuel utilization within a safe range, which affects their lifespan. To address this issue, we propose a Data-driven Output voltage control (DDD-OVC) approach that treats the SOFC output voltage controller as an intelligent agent. The agent can design a reward function that incorporates the fuel utilization constraint and learn the optimal control policy through offline training. The objective is to optimize both the SOFC output voltage control performance and lifetime. Furthermore, we develop a policy optimization-based Deep Reinforcement Learning (PO-DRL) algorithm that adopts the idea of proximal policy optimization to enhance the learning speed and convergence of the agent, as well as the policy stability and quality of DDD-OVC. We validate the effectiveness of our method on a 5kW SOFC model.

Adaptive Coordination Control Based on Alkaline Water Electrolyzers and Battery Driven by Wind Power

With the widespread use of clean energy sources, there has been a growing interest in obtaining green and pure hydrogen from wind power. However, wind energy is characterized by randomness and volatility, which results in a low grid connection rate for wind turbines and makes it difficult to integrate wind energy into the power grid. In this context, this article proposes an adaptive coordinated control strategy considering the wind turbine, alkaline electrolyzer, battery, and local loads. Using the DC bus voltage as the connecting signal, adaptive power control and voltage control methods are developed for the wind turbine, energy storage battery, and electrolyzer to achieve power matching in the system. Finally, the proposed research results are validated through Matlab/Simulink simulation module.

Flexible composite suppression method for ground arc in resonant grounding system

AbstractA current‐voltage composite controller for an arc suppression inverter is designed. The current and voltage at the fault point are reduced by injecting current and regulating the neutral point voltage to completely eliminate the risk of fire and overvoltage caused by a single‐line‐to‐ground fault arc. A cascaded inverter is used, and the effects of the ground parameter measurement, line impedance voltage drop, harmonic current and arc suppression coil on the current control, voltage control and composite control methods are analysed and compared. Finally, software simulations and industrial prototype experiments show that the proposed composite control method is effective and less affected by ground parameter measurement and line impedance voltage drop and has better adaptability.

A Multi-Agent Reinforcement Learning Method for Cooperative Secondary Voltage Control of Microgrids

This paper proposes a novel cooperative voltage control strategy for an isolated microgrid based on the multi-agent advantage actor-critic (MA2C) algorithm. The proposed method facilitates the collaborative operation of a distributed energy system (DES) by adopting an attention mechanism to adaptively boost information processing effectiveness through the assignment of importance scores. Additionally, the algorithm we propose, executed through a centralized training and decentralized execution framework, implements secondary control and effectively restores voltage deviation. The introduction of an attention mechanism alleviates the burden of information transmission. Finally, we illustrate the effectiveness of the proposed method through a DES consisting of six energy nodes.

Coordinated control of multiple converters in model-free AC/DC distribution networks based on reinforcement learning

Taking into account the challenges of obtaining accurate physical parameters and uncertainties arising from the integration of a large number of sources and loads, this paper proposes a real-time voltage control method for AC/DC distribution networks. The method utilizes model-free generation and coordinated control of multiple converters, and employs a combination of agent modeling and multi-agent soft actor critic (MASAC) techniques for modeling and solving the problem. Firstly, a complex nonlinear mapping relationship between bus power and voltage is established by training an power-voltage model, to address the issue of obtaining physical parameters in AC/DC distribution networks. Next, a Markov decision process is established for the voltage control problem, with multiple intelligent agents distributed to control the active and reactive power at each converter, in response to the uncertainties of photovoltaic (PV) and load variations. Using the MASAC method, a centralized training strategy and decentralized execution policy are implemented to achieve distributed control of the converters, with each converter making optimal decisions based on its local observation state. Finally, the proposed method is verified by numerical simulations, demonstrating its sound effectiveness and generalization ability.

A continuous-time voltage control method based on hierarchical coordination for high PV-penetrated distribution networks

The photovoltaic (PV) integration brings both fast photovoltaic generation (PVG) variations and a large number of PV inverters to the distribution networks (DNs). The management of the PVG variation and the PV inverter is an urgent problem. The discrete-time model can’t describe PVG variations during the schedule interval and the regulation potential of the PV inverter is limited by a fixed dispatching value. This paper proposes a continuous-time voltage control method based on hierarchical coordination for high PV-penetrated DNs. In the central schedule, based on the Bernstein polynomial method, continuous-time models of PVGs are established and reference values of local inverter control curves are dispatched as continuous-time functions in a sub-hourly timescale. In the local control, reactive power and active power curtailment of PV inverters are set as the reference values, i.e., the corresponding values of the continuous-time functions, every 30 s to optimize the central dispatching objective under the voltage constrains, and deviate from the reference values according to both the local control curve and voltage measurement only when the real-time bus voltage violation occurs. In addition, the cooperation between inverter and mechanical equipment in the continuous-time framework is also considered, and the Bernstein polynomial method is developed to the continuous-time modeling of voltage control in DNs. The simulation shows that the proposed method has lower maximum voltage magnitude and lower energy loss compared with discrete-time central schedule method.

The Application of Photovoltaic-Electric Spring Technology to Rural Power Grids

Rural power grids are essential for rural development, impacting the lives of farmers, the agricultural economy, and the overall efficiency of agricultural production. To ensure the reliable operation of these grids, finding ways to provide high-quality power is imperative. In recent years, the penetration rate of distributed photovoltaic (PV) in the distribution network has been increasing. When the output of PV and load are not matched, the voltage fluctuation of the network affects the safe and stable operation of the distribution network. In this study, we propose that the stable operation of rural power grids can be achieved by employing a photovoltaic-electric spring (PV-ES) device. A state space model of PV-ES is established and a single PV-ES voltage control method, based on a PI controller, is proposed, taking a rural user household with a monthly power consumption of about 120 access to distributed power supply as an example. We analyzed the device’s effectiveness in addressing voltage fluctuation issues as well as how light intensity impacts its effectiveness. The implementation of the PV-ES device solves the most significant problem faced by rural power grids, namely, the unstable power supply that occurs during peak electricity consumption periods. In addition, the PV-ES device ensures a high-quality electricity consumption experience for consumers.

Mixed Linear Model of a Safety Dispatch Model in an Active Distribution Network for Source–Grid–Load Interactions

There are a large number of plug-and-play loads in an active distribution network, such as EVs(Electric Vehicles), energy storage, solar power, etc. Due to the lack of security control methods for each terminal node and the lack of distributed power voltage control methods, the large number of loads brings a huge challenge to the security of the distribution network. At present, some regional distribution networks dominated by new energy in China have long-standing problems, such as high voltage impact and frequency flickering, which are extremely harmful to electric equipment, and the resulting load-side accidents have brought huge economic losses. Therefore, research on an optimization model of the source–grid–load interaction in the active distribution network considering the safety characteristics, especially the voltage of the system, will help to improve the quality of the grid dispatch. In this paper, the safety limits of the independent operation of a source network loaded on three sides are used as the operating constraints of the system, and the social welfare of the interaction is maximized as the goal. A joint optimization modeling after the independent solution of the three sides is used as the core means, a heuristic algorithm is used to solve the overall optimization of the whole system, a scheduling optimization model that meets the system security goals is constructed, and this model is used to guide the operation strategy of each node in the system. The Lagrangian relaxation factor is introduced for structural optimization, and finally, the simplified 36-node model of the actual power grid is used for verification. The results show that under the goal of ensuring the economy of the system, the system voltage is controlled within the specified range of the safe operation of the system, which can meet the safety needs of the interaction.

Bandwidth oriented approach for the design of a PI controller for a three phase two-stage grid-connected PV system

ABSTRACT The two-stage power converter is currently the most widespread topology for three-phase grid-connected photovoltaic (PV) systems. The PV power is injected to the grid through the regulation of the dc-link voltage. This method is commonly called the voltage control method, whereby the current is controlled indirectly. This paper focuses on the modelling of power converters and the parameter design of proportional-integral (PI) controllers, based on the bandwidth of the inner current loop and the outer voltage loop. It is assumed that the inner loop has a bandwidth that is ten-fold bigger than that of the outer loop, in order to fully decouple the dual loops. The PI parameters are thus determined through this relationship, and two sets of simulations are run to validate the methodology used. Results show that the controllers give highly satisfactory performance even in the presence of transients introduced by rapidly varying irradiance model.

Overview of Various Voltage Control Technologies for Wind Turbines and AC/DC Connection Systems

Wind power generation is one of the mainstream renewable energy resources. Voltage stability is as important as the frequency stability of a power system with a high penetration of wind power generation. The advantages of high-voltage direct current (HVDC) transmission systems become more significant with the increase of both installed capacity and transmission distance in offshore wind farms. Therefore, this study discusses various voltage control methods for wind turbines and HVDC transmission systems. First, various voltage control methods of a wind farm were introduced, and they include QV control and voltage droop control. The reactive power of a wind turbine varies with active power, while the active power from each wind turbine may be different owing to wake effects. Thus, QV and voltage droop control with varying gain values are also discussed in this paper. Next, the voltage control methods for an HVDC transmission system, such as power factor control, voltage control, and Vac-Q control, are also summarized and tested in this study. When a three-phase short circuit fault occurs or a sudden reactive power load increases, the system voltage would drop immediately. Thus, various voltage control methods for wind turbines or HVDC can make the system’s transient response more stable. Therefore, this study implemented the simulation scenarios, including a three-phase short circuit fault at the point of common coupling (PCC) or a sudden increase of reactive power load, and adopted various voltage control methods, which aim to verify whether additional voltage control methods are effective to improve the performance of transient voltage. The voltage control method has been implemented in PSCAD/EMTDC, and the simulation results show that the QV control performs better than the droop control. In addition, when applying the voltage control technique during a three-phase fault, transient voltage nadir can be improved through either an HVDC transmission system or an AC transmission system.

A bi-level optimization method for voltage control in distribution networks using batteries and smart inverters with high wind and photovoltaic penetrations

In electrical distribution systems, the voltage quality problem considers the most restrictive issue that hinders high photovoltaic (PV) and wind integration. Therefore, this study aims at providing a reliable control method for the voltage problem in distribution networks embedded with high PV and wind sources. The proposed voltage management strategy consists of two controlling stages. The first stage uses battery energy storage systems (BESS), while the second stage employs the smart PV inverters' reactive power injection capability. For BESS operation, a bi-level optimization method based on metaheuristic optimization algorithms (MOA) is developed to regulate the voltage levels by governing the batteries charging/discharging rates. The bi-level optimization problem aims to maximize PV and wind energy exploitation while improving the voltage profile. In this study, a detailed comparison is conducted between three MOA: Social Spider Optimization, Particle Swarm Optimization, and Cuckoo Search Optimization. In addition, considering the uncertainty associated with PV system generation, a PV generation forecast is embedded in the voltage control strategy to reinforce the decision-making process of BESS operation. The efficacy of the proposed method is deployed on a modified IEEE-34 bus test feeder with real-world load and PV and wind power generation data. Experimental results show the effectiveness of the proposed method and indicate that proper coordination between the BESS and smart PV inverters is beneficial for distribution system operations that seamlessly integrate PV and wind energy. Overall, the proposed voltage management strategy can be used to control the voltage to any desired range at different locations.

Probabilistic approach to investigate the impact of distributed generation on voltage deviation in distribution system

The increasing penetration of renewable-based generation in the distribution network adds randomness to the bus voltage due to its stochastic characteristics. Consequently, the distribution network faces voltage limit violations at multiple buses. In this scenario, the traditional voltage control method fails to maintain the voltage profile of the network due to its slow response time. A fast and dynamic voltage control technique can be achieved by utilizing the voltage-controlling capability of available DGs in the network with the information about the impact of DG-integrated bus on change in voltage at any bus in the network. Evaluating this information using the traditional probabilistic load flow method is computationally inefficient for real-time applications. Therefore, in this paper, a principal component analysis (PCA)-based novel probabilistic voltage sensitivity index (PVSI) is proposed. It ranks the DG-integrated buses by evaluating the impact of power perturbation at DG-integrated buses on changes in voltage at any bus in the network. The PVSI is analytically derived to reduce the computation time for ranking. Further, the proposed PVSI is validated on the 69-bus and the 141-bus distribution systems by comparing it with the traditional Monte Carlo simulation (MCS) and joint differential entropy (JDE) in terms of accuracy and computation time. From the simulation, it is observed that PVSI gives a similar ranking as both methods in less computation time. Due to the advantage in computation time, the proposed method enables the distribution system operator (DSO) to use this information for fast and dynamic voltage control in a real-time scenario.

Optimal Voltage Control of Wind Farm with Distributed Energy Storage System

This paper proposes an optimal voltage control method for a wind farm (WF) combined with distributed energy storage systems (DESSs), where the DESSs are connected to the DC sides of wind turbines (WTs). In the proposed control method, the WT units and DESSs can be coordinated to minimize the voltage fluctuation of terminal buses, minimize the distance between the voltage of terminal buses and rated value, and minimize power losses inside the WF collection system. The optimal control (OPC)-based voltage control model is formulated to generate optimal active/reactive power output of WTs and active power output of energy storage units in the WF with DESSs. The DESSs can store active power from WTs to support more capacity of the reactive power output of WTs. The control performance of the proposed voltage control method based on OPC is validated by conducting case studies on the WF with 32*5 MW WTs.

Emulated Inertia Control of Grid-connected Inverter-based Power Supply Sources for Mass Integration of Renewable Energy Resources

This study investigated the important elements of emulated inertia control for grid-connected inverter-based power supply sources and identified the issues to be addressed for its integration into the electric power transmission and distribution system. For the first time, we categorized the emulated inertial control algorithms into five types based on three different aspects. Five emulated inertia control algorithms were simulated in order to compare their effectiveness in suppressing frequency fluctuations. In addition, a representative emulated inertia control algorithm based on the voltage-controlled method and another based on the current-controlled method were distinguished. The two emulated inertia control algorithms were implemented and demonstrated using the experimental hardware of a 5 [kVA] inverter-based power supply.

Hierarchical Central-Local Inverter-Based Voltage Control in Distribution Networks Considering Stochastic PV Power Admissible Range

This paper proposes a hierarchical distribution network voltage control method considering active and reactive power coordination of PV units in both central and local control stages. In contrast to the traditional local control methods, the proposed method defines the admissible range (AR) of PV power and determines it via centralized optimization to realize active PV power curtailments in the local control stage. The affine decision rule (ADR) is adopted to control the reactive power with respect to active power within AR. A distributionally robust chance constraint is designed, based on statistical indices of active PV power, to assess the probability that active PV power generation would fall inside AR. A two-stage optimization problem that can simultaneously provide central and local control strategies is proposed, which is transformed into tractable formulas based on the Gauss Inequality as well as ADR and binary expansion techniques. The proposed method solutions are compared with those of the theoretically optimal method, a robust central-local control method, and a local control method to show the value of hierarchical inverter control with AR in reducing PV power curtailment and ensuring nodal voltages within limits.