Power System Voltage Control Research Articles

Impact of ABT based control strategies on ALFC and AVR in DFIG-integrated interconnected power systems

To maintain a stable electricity supply in interconnected power grids, effective frequency and voltage regulation are required. Interconnected power systems use Automatic Load Frequency Control (ALFC) and Automatic Voltage Regulator (AVR) for this purpose. The integration of renewable sources, such as Doubly Fed Induction Generators (DFIGs), creates challenges for such control loops. This study investigates the impact of using various Frequency-Linked Pricing (FLP)-based operating strategies under Availability-Based Tariff (ABT) on combined ALFC and AVR in a two-area interconnected power system with DFIGs. FLP-based operating strategies under ABT react to frequency changes through price signals, which can act faster compared to traditional AGC methods that rely on communication between control centers. This quicker response helps maintain frequency and voltage during load disturbances. Most of the earlier research focused mostly on using only a single type of FLP-based operating strategy under ABT within the ALFC loop, neglecting the combined ALFC-AVR interaction. This work addresses this gap by evaluating three FLP-based operating strategies under ABT along with the traditional approach for their impact on frequency and voltage under step load disturbance. Simulation studies conducted in MATLAB 2022a demonstrate the effectiveness of the proposed strategies, providing valuable insights for improving frequency and voltage control in modern power systems. The investigation reveals that the strategy that considers the difference between the actual and reference values of the unscheduled interchange (UI) charge and the marginal cost of generation to produce the control command outperforms the other strategies and restores the frequency and voltage to their nominal values.

Decentralized Robust Emergency Voltage Control of Power System with Large-scale Wind Power Integrated via VSC-MTDC

Decentralized Robust Emergency Voltage Control of Power System with Large-scale Wind Power Integrated via VSC-MTDC

Terminal Voltage and Load Frequency Control in a Real Four-Area Multi-Source Interconnected Power System With Nonlinearities via OOBO Algorithm

Terminal Voltage and Load Frequency Control in a Real Four-Area Multi-Source Interconnected Power System With Nonlinearities via OOBO Algorithm

Distributed model predictive control based on the alternating directions method of multipliers applied to voltage and frequency control in power systems

AbstractWe present a straightforward way to solve a model predictive control problem for a power network system given as a nonlinear differential‐algebraic equation (DAE) in a distributed way using the consensus alternating directions method of multipliers (consensus ADMM) algorithm. While no convergence‐ or stability results are available for fully nonlinear DAE models, this gives unprecedented experimental evidence that power network systems of the presented structure allow to be controlled in this way, unlocking the numerous combined advantages of distributed and predictive control schemes in the context of energy distribution networks, as well as broadening the field of use for the consensus ADMM algorithm to nonlinear DAE models.

Deep reinforcement learning based voltage control revisited

AbstractDeep Reinforcement Learning (DRL) has shown promise for voltage control in power systems due to its speed and model‐free nature. However, learning optimal control policies through trial and error on a real grid is infeasible due to the mission‐critical nature of power systems. Instead, DRL agents are typically trained on a simulator, which may not accurately represent the real grid. This discrepancy can lead to suboptimal control policies and raises concerns for power system operators. In this paper, we revisit the problem of RL‐based voltage control and investigate how model inaccuracies affect the performance of the DRL agent. Extensive numerical experiments are conducted to quantify the impact of model inaccuracies on learning outcomes. Specifically, techniques that enable the DRL agent are focused on learning robust policies that can still perform well in the presence of model errors. Furthermore, the impact of the agent's decisions on the overall system loss are analyzed to provide additional insight into the control problem. This work aims to address the concerns of power system operators and make DRL‐based voltage control more practical and reliable.

Artificial emotional deep Q learning for real-time smart voltage control of cyber-physical social power systems

Artificial emotional deep Q learning for real-time smart voltage control of cyber-physical social power systems

Epistemology of voltage control in DER-rich power system

Despite the recent development of several scalable, robust, and resilient control approaches with superior convergence properties considering an increasing penetration of distributed energy resources (DERs), cognitive oversights often simplify several aspects of the cyber–physical power system in the controller development. Following the identification of the limitations of classical controller definitions, we justify alternative definitions of voltage control approaches classifiers considering three inter-disciplinary domains: (i) power system, (ii) optimization and decision-making, and (iii) networking and cyber-security, to develop a taxonomy for helping in real-world comparative performance analysis and deployability of these controllers. We observe that classical and introduced domain-based definitions together can better classify the control algorithms.

Voltage control in a wind-diesel power system using adaptive RBF sliding mode control of STATCOM

Voltage control in a wind-diesel power system using adaptive RBF sliding mode control of STATCOM

Voltage control in a wind-diesel power system using adaptive RBF sliding mode control of STATCOM

Voltage control in a wind-diesel power system using adaptive RBF sliding mode control of STATCOM

Operation Issues and Data-Driven Voltage Control in Agile Power Systems

This paper defines an agile power system as a system in which the physical structure can change due to the connection/disconnection of subsystems, such as microgrids. These systems pose serious difficulties in their operations and control. Some of these challenges are reviewed. One operational issue is the energy balancing or the load frequency control problem when microgrids with a low inertia are connected to the main grid. The paper identifies the need for new rules of operation. Furthermore, in this paper, decentralized and data-driven voltage control designs are proposed, compared and illustrated on a microgrid. They take into consideration the structure and the usage of the large amount of data provided by the proliferation of sensors.

Physics-Informed Evolutionary Strategy Based Control for Mitigating Delayed Voltage Recovery

In this work we propose a novel data-driven, real-time power system voltage control method based on the physics-informed guided meta evolutionary strategy (ES). The main objective is to quickly provide an adaptive control strategy to mitigate the fault-induced delayed voltage recovery (FIDVR) problem. Reinforcement learning methods have been developed for the same or similar challenging control problems, but they suffer from training inefficiency and lack of robustness for “corner or unseen” scenarios. On the other hand, extensive physical knowledge has been developed in power systems but little has been leveraged in learning-based approaches. To address these challenges, we introduce the trainable action mask technique for flexibly embedding physical knowledge into RL models to rule out unnecessary or unfavorable actions, and achieve notable improvements in sample efficiency, control performance and robustness. Furthermore, our method leverages past learning experience to derive surrogate gradient to guide and accelerate the exploration process in training. Case studies on the IEEE 300-bus system and comparisons with other state-of-the-art benchmark methods demonstrate effectiveness and advantages of our method.

Comparative performance of multiple energy storage systems in unified voltage and frequency regulation of power system including electric vehicles

AbstractModern power systems are very complicated and have more obligation to uphold the supply of power be reliable, stable, and of decent quality at both the transmission and distribution phases. Preserving grid balance is a significant concern if there is an unforeseen generation deficit or grid disturbance, or intermittent renewable energy sources, such as bio‐diesel, electric vehicles (EVs), and solar power are incorporated into the energy mix. To compensate for this imbalance and enhance the power system's reliability and stability, the energy storage systems (ESS) can be regarded as an appropriate measure. Thus, ESS contributes to greenhouse gas (GHG) emission reductions by successfully integrating more renewable energy sources into the grid. This article presents multiple ESSs, such as pumped hydroelectric storage (PHS), accurate flywheel energy storage (AFES), battery energy storage (BES), capacitive energy storage (CE), and superconducting magnetic energy storage (SMEs) and their comparative performance analysis in unified voltage and frequency control of power system. The proposed interconnected power system includes thermal, solar‐thermal, bio‐diesel‐engine generator, and EVs. In both control systems, a maiden attempt was made to utilize Harris' Hawks optimization‐based fuzzy‐fractional‐order‐tilt‐integral‐derivative (FFOTID) controller. The repercussion of PHS proclaims a substantial amelioration in system performance than other ESSs. Likewise, the system exhibits better dynamics with the coalition of PHS and power system stabilizer (PSS) of AVR compared to their separate intervention. Lastly, sensitivity analysis of proposed controller is analyzed by alteration of load perturbation and nonlinearity of thermal plants, which ensures optimal controller parameters accomplished at nominal conditions are resilient enough.

A Dynamic Adjustment Method of Dead Band Width for Advanced Voltage Control in Power Systems

A Dynamic Adjustment Method of Dead Band Width for Advanced Voltage Control in Power Systems

Consensus Admm-Based Distributed Mpc Applied to Voltage and Frequency Control in Power Systems

Consensus Admm-Based Distributed Mpc Applied to Voltage and Frequency Control in Power Systems

Supervised assisted deep reinforcement learning for emergency voltage control of power systems

The increasing complexity of power systems makes existing deep reinforcement learning-based emergency voltage control methods face challenges in learning speed and data utilization efficiency. Meanwhile, the accumulated data containing expert experience and domain knowledge has not been fully utilized to improve the performance of the deep reinforcement learning methods. To address the above issues, a novel hybrid emergency voltage control method that combines expert experience and machine intelligence is proposed in this paper. Specifically, the expert experience in the off-line demonstration is extracted through a behavioral cloning model and the deep reinforcement learning method is applied to discover and learn new knowledge autonomously. A special supervised expert loss is designed to utilize the pre-trained behavioral cloning model to assist the self-learning process. The demonstration is dynamically updated during the training process such that the behavioral cloning model and the deep reinforcement learning model can facilitate each other continuously. Experiments are conducted on the open-source RLGC platform to validate the performance and the experimental results show that the proposed method can effectively improve the learning speed and the applicability of the model to different test situations.

Simultaneous analysis of frequency and voltage control of the interconnected hybrid power system in presence of FACTS devices and demand response scheme

This work confers the simultaneous analysis of voltage and frequency control of the 3-area interconnected hybrid power system (IHPS) consisting of parabolic-trough solar power system (PSP), wind power system (WPS) and dish-stirling solar power system (DSP) under the paradigm of microgrid. The speculated result of the IHPS is presented and analyzed considering real and reactive power as the function of both voltage and frequency. 9The proposed IHPS under investigation has been mathematically modeled for direct coupling like active power–frequency and reactive power–voltage relationships and cross coupling like active power–voltage and reactive power–frequency​ relationships. The system responses under different operating conditions have been investigated to see the cross-coupling behavior of the proposed IHPS in the presence of voltage compensating devices like dynamic voltage restorer (DVR) and Static Synchronous Compensator (STATCOM). Further, Demand Response Scheme (DRS) as a frequency control strategy has been considered to enhance the system stability. System responses have been critically analyzed under Mine Blast Algorithm (MBA) based proportional–integral–derivative (PID) controllers

Design of combine load frequency and voltage control of power system using optimisation algorithm

Engineers' main concern is the stability of nominal frequency and voltage levels in an electric power system. If these two characteristics deteriorate, the performance and life expectancy of the power system's related equipment would suffer. Because of this, control devices must be set up and installed specifically for a given working environment in order to maintain the frequency and terminal voltage magnitude within acceptable ranges. An effort has been made to develop a fractional-order PID (FOPID) controller for combined frequency and voltage control issues, since the system performance may be enhanced by choosing an appropriate controller. Utilizing moth flame optimization method, this study examines the plan and execution of the FOPID controller for simultaneous load frequency and voltage management of the power system. An AVR-based excitation voltage control system is used in the first portion of the study to illustrate the suggested method for frequency stabilization of isolated power systems. Comparison of dynamic responsiveness of the system with PID controllers improved by other intelligent methods reveals the superiority and efficacy of the suggested methodology. Also included in this project are multi-unit two-area power systems. The algorithm's capacity to be tuned is examined in great detail and compared to other algorithms.

A quick solution to optimal coordinated voltage-control, based on dimension-reduction of power system via Modified Ward-PV

Voltage control and Voltage stability are the most important issues in the power system. The purpose of this paper is to investigate the emergency voltage control in power systems. One of the most important proposed methods to solve this issue is optimal coordinated voltage control (OCVC), which is based on the model predictive control (MPC). Time limitations for preventing voltage collapse is the most critical constraint to solve an OCVC problem. In this paper, the Modified WARD-PV will be proposed to reduce the dimension of the power system and consequently, the speed of solving problem is increased. In this method, immediately after occurrence a fault, the power system is partitioned to into three subsystems by spectral graph partitioning method. Partitioning is based on reactive power flow through the lines. The subsystems, which are far from fault, will be replaced by the reduced model. This method will drastically reduce the number of system equations and will boost the speed of solving problem. The simulations results obtained on New England IEEE 39-Bus System reveals that the proposed method acts more precise than dimension reduction of power system based on linearization of external subsystems, but the speed of solving problem decrease.

Emotional Deep Learning Programming Controller for Automatic Voltage Control of Power Systems

In recent years, the rapid development of artificial intelligence, especially deep learning technology, makes machine learning have application scenarios in the fields of power system stability analysis, coordination along with scheduling and load forecasting. This paper designs an emotional deep learning programming controller (EDLPC) for automatic voltage control of power systems. The designed EDLPC contains an emotional deep neural network (EDNN) structure and an artificial emotional Q-learning algorithm. Besides, a specially defined proportional-integral-derivative (PID) controller is added to the deep neural networks (DNNs) structure as the actuator of an EDNN to realize the automatic tuning of PID controller parameters. In terms of control, the controller combines the advantages of the EDNN and PID controller, meanwhile adopts a reinforcement learning algorithm to optimize the parameters. From the perspective of reinforcement learning, embedding prior knowledge into the output instructions of EDNN is helpful to weaken the fitting problem in the training process. Compared with the outputs of the DNN and Q-learning algorithm under the two cases, the EDLPC could gain the highest control performance with smaller voltage deviations. The simulation results verify the feasibility and effectiveness of the proposed method for automatic voltage control of power systems.

Secondary and Tertiary Voltage Control of a Multi-Region Power System

This paper presents techniques for the application of tertiary and secondary voltage control through the use of intelligent proportional integral derivative (PID) controllers and the wide area measurement system (WAMS) in the IEEE 39 bus system (New England system). The paper includes power system partitioning, pilot bus selection, phasor measurement unit (PMU) placement, and optimal secondary voltage control parameter calculations to enable the application of the proposed voltage control. The power system simulation and analyses were performed using the DIgSILENT and MATLAB software applications. The optimal PMU placement was performed in order to apply secondary voltage control. The tertiary voltage control was performed through an optimal power flow optimization process in order to minimize the active power losses. Two different methods were used to design the PID secondary voltage control, namely, genetic algorithm (GA) and neural network based on genetic algorithm (NNGA). A comparison of system performances using these two methods under different operating conditions is presented. The results show that NNGA secondary PID controllers are more robust than GA ones. The paper also presents a comparison between system performance with and without secondary voltage control, in terms of voltage deviation index and total active power losses. The graph theory is used in system partitioning, and sensitivity analysis is used in pilot bus selection, the results of which proved their effectiveness.