Grid Control Research Articles

WAMS-Based Fuzzy Logic PID Secondary Voltage Control of the Egyptian Grid

This paper presents the application of fuzzy logic PID secondary voltage control to the Egyptian power system model. The study included tertiary voltage control, Wide Area Measurement System (WAMS) configuration, a selection of pilot buses, and fuzzy logic PID secondary voltage control to improve the system performance. The secondary voltage control was applied using a fuzzy PID coordinated controller, a reactive power integral controller, Automatic Voltage Regulators (AVRs), and regional generators. The tertiary voltage control was implemented based on the optimal power flow to maximize the reactive power reserve. A novel optimization technique is presented to select pilot buses based on different operating conditions and compared to other techniques. The optimal WAMS configuration included the best allocation of Phasor Measurement Units (PMUs), Phasor Data Concentrators (PDCs), and the required communication infrastructure considering geographical regions with minimum cost. The Egyptian power grid considering 500/220 kV level is simulated by using DIgSILENT software to perform static and dynamic analyses, while the WAMS optimization problems and fuzzy logic PID controller design are performed by employing MATLAB software.

Cyber-physical cascading failure and resilience of power grid: A comprehensive review

Smart grid technologies are based on the integration of the cyber network and the power grid into a cyber-physical power system (CPPS). The increasing cyber-physical interdependencies bring about tremendous opportunities for the modeling, monitoring, control, and protection of power grids, but also create new types of vulnerabilities and failure mechanisms threatening the reliability and resiliency of system operation. A major concern regarding the interdependent networks is the cascading failure (CF), where a small initial disturbance/failure in the network results in a seemingly unexpected large-scale failure. Although there has been a significant volume of recent work in the CF research of CPPS, a comprehensive review remains unavailable. This article aims to fill the gap by providing a systematic literature survey regarding the modeling, analysis, and mitigation of CF in CPPS. The open research questions for further research are also discussed. This article allows researchers to easily understand the state of the art of CF research in CPPS and fosters future work required towards full resolutions to the remaining questions and challenges.

Cooley-Tukey FFT Algorithm based on GDFT for Phasor Estimation by PMU under Power Quality Disturbances

Due to the high penetration of renewable energy sources, such as photovoltaic panels and wind turbines, in addition to the use of different electric power supplies in the power grid, there are major disturbances in the forms of electric waves. These variations and disturbances must be monitored and controlled for the efficient management of transmission and distribution of electrical energy, safety, and electrical protection systems. Nowadays, Phasor Measurement Unit (PMU) technology is an essential tool to develop the supervision, protection, and control of the electrical power grid. PMUs measure the amplitude and angle of current and voltage waveforms on a Coordinated Universal Time (UTC) time scale and speedily measure the fundamental frequencies and their rates of change using fast and accurate estimation algorithms. This paper presents a phasor estimation using a Modified Cooley-Tukey Fast Fourier Transform algorithm based on the Generalized Discrete Fourier Transform (DFT) used in PMUs, using simulations in MATLAB. This algorithm was utilized to accelerate and simplify the computation of DFTs. To validate the performance under waveform disturbances, several tests with different waveforms and disturbances were simulated and interpreted according to the standard and compared with DFT.

Study on Coordinated Control of New Energy Over-Frequency Ride-Through and Over-Frequency Generator-Tripping Scheme

After the fault isolation of the outgoing power grid with a high proportion of new energy, the high-frequency shutdown of the third line of defense is an important control measure to restrain the rise of the frequency of the sending-end power grid. With the increase in the proportion of intermittent power sources such as wind power and photo-voltaic power, the frequency characteristics of the power grid have become more complex. Considering the response characteristics and emergency control capabilities of wind power and photo-voltaic power, this paper proposes a high-frequency ride-through control strategy based on new energy stations. Through coordinated control with high-frequency shutdown measures, the new energy stations can rapidly reduce their power to replace some costly conventional unit operations to achieve more economical frequency safety control, after the high-frequency machine switching round action is executed to avoid over-switching triggering low cycle load shedding when the frequency is reduced to the threshold value, it will trigger the rapid recovery of new energy power generation to meet the requirements of refined frequency stability control of modern power grids. Based on the actual planning of the high-voltage transmission end grid scale, a simulation system was built, and a new energy high-frequency ride-through model was built, which verified the feasibility of the proposed coordinated control scheme for high-frequency switching off with a high-frequency ride-through, and verified the adaptability of the proposed scheme under different operation modes.

Optimization of Basepoint Configuration in Localization of Signal Interference Device

Precise navigation, as a method for guiding vehicles from one point to another, is an important subject these days especially in navigation of aircraft. Global navigation satellite systems (GNSSs) are capable tools for such a purpose. Any intentional or unintentional interference in satellite signals may cause risks of deadly accidents. Therefore, it is tremendously important to control airports or harbors and locate any existing radio frequency interference device. This localization can be done based on measuring time of arrival (TOA), angle of arrival (AOA), or time difference of arrival (TDOA) of signals from the device to sensors or receivers at some basepoints. In this article, a method is proposed based on these arrivals for optimizing the configuration created by these basepoints from a large grid of points covering a control area. Furthermore, a simulation test was performed to verify the theory, and after that a control network was designed and optimized for the international Landvetter Airport of Sweden. Our simulation studies show that when the AOA is used, our optimization is more robust with respect to the control grid resolution. In addition, optimization based on the TDOA improves the coverage over the control area with a significant reduction of error of control points, but because of the special geometric shape of the Landvetter Airport, such an optimization was not successful.

Primary and Secondary Control of Grid Connected Micro Grids using ANFIS Controller

The microgrid has been defined to cope with the large penetration of distributed generations (DGs). The microgrid is capable of operating in grid-connected mode (GC) and islanded mode (IS). In microgrid, distributed generations (DGs) are always connected to power network via power inverters. In the Grid Connected mode, DGs commonly acts as current controlled sources to retain a high grid-current quality and a fast dynamic response in the photovoltaic and wind generation systems. However, this current controlled DGs cannot work alone without the voltage/frequency support from utility-grid. Islanding occurs when a portion of a micro-grid becomes electrically isolated, yet continues to be energized by DGs connected to the isolated subsystem. In islanding mode, the inverter-interfaced DGs work as voltage-controlled sources. As system voltage and frequency are not determined by utility-grid, DGs should take care of the voltage/frequency stability. Moreover, the power sharing should be guaranteed according to their individual ratings to avoid circulating currents among DGs. The operation and control of existing utility networks are becoming more and more complex due to the increased interconnection of a DG systems with diverse characteristics. So this paper intends to address the above problems and come out with a proper control strategy using ANFIS controller that could be implemented for the successful operation of a microgrid.

An Analysis of Multilevel Converter for Faster Current Control in a DC Micro grid with Extremely Low- Impedance Interconnections

Improved power converter configurations and semiconductors innovation capable of driving the required power have emerged in response to the rising global need for energy. It is still a constant endeavour to create semiconductors with greater current or voltage power to propel high power systems. In this way, modern gadgets can handle high voltage or current with ease. Traditional high-voltage semiconductor-based power converter schemes face stiff opposition from innovative medium-voltage device-based alternatives. DC microgrids, with their very limited line impedance, are developing as the upcoming type of small-scale power transmission connections. Due to this phenomenon, even a little change in voltage can create significant currents in microgrids, making quick rapid reaction and accurate power flow regulation essential. In order to provide fast and precise power flow regulation in a dc microgrid, this research employs multi-level converters as the controllers. A multilevel converter allows for a compact output filter. In addition, the final LC filter of a MLC that satisfies a current ripple demand has been designed and is presented in this work. In comparison to traditional two-level converters, we were able to demonstrate that a multi-level converter with a more compact filter may provide high-speed & high-precision power flow regulation under low line impedance situations. Using the simulation results from MATLAB/Simulink, the overall performance of every output current is assessed in the system response, taking into account the transient variations in the power flow.

A Comprehensive Analysis of Smart Grid Stability Prediction along with Explainable Artificial Intelligence

As the backbone of modern society and industry, the need for a more efficient and sustainable electrical grid is crucial for proper energy management. Governments have recognized this need and have included energy management as a key component of their plans. Decentralized Smart Grid Control (DSGC) is a new approach that aims to improve demand response without the need for major infrastructure upgrades. This is achieved by linking the price of electricity to the frequency of the grid. While DSGC solutions offer benefits, they also involve several simplifying assumptions. In this proposed study, an enhanced analysis will be conducted to investigate how data analytics can be used to remove these simplifications and provide a more detailed understanding of the system. The proposed data-mining strategy will use detailed feature engineering and explainable artificial intelligence-based models using a public dataset. The dataset will be analyzed using both classification and regression techniques. The results of the study will differ from previous literature in the ways in which the problem is handled and the performance of the proposed models. The findings of the study are expected to provide valuable insights for energy management-based organizations, as it will maintain a high level of symmetry between smart grid stability and demand-side management. The proposed model will have the potential to enhance the overall performance and efficiency of the energy management system.

An intelligent analysis method of security and stability control strategy based on the knowledge graph

The security and stability control system is the guarantee of the security and stability operation of the power grid. With the increasing scale of distributed new energy access to the power grid, the security and stability control strategy of the power grid is becoming more complex, and it is becoming increasingly important to correctly analyze and implement the security and stability control strategy. In order to ensure the correctness of the security and stability control strategy implemented by the security and stability control device, it is necessary to analyze the security and stability control strategy in detail. Therefore, this article proposes an intelligent analysis method of the security and stability control strategy based on the knowledge graph. First, this article introduces the ontology design method of the security and stability control strategy based on the knowledge graph, combines the characteristics and applications of the knowledge graph, analyzes the relationship between the elements of the strategy, and designs a clear-structured knowledge network. Second, this article analyzes the automatic construction technology of the graph, constructs the six-element ontology model of the security and stability control strategy, and realizes the human–computer interaction functions such as auxiliary decision making, strategy reasoning, and intelligent search based on the knowledge graph. Using artificial intelligence technology, this article takes the security and stability control strategy of a certain area’s security and stability control system as an example to model and manage. The results show that it can assist the tester to quickly retrieve the strategy, effectively improve the detection efficiency of the security and stability control strategy, avoid the omission and ambiguity caused by the manual understanding of the strategy, and ensure the accuracy and comprehensiveness of the security and stability control strategy detection.

Real-time Volt-Var control of grid forming converters in DER-enriched distribution network

The growing installation of distributed energy resources (DERs) in a distribution network (DN) poses substantial issues related to voltage regulation. Due to constrained switching operation and slower response time, traditional voltage regulation devices cannot handle current voltage-related challenges. One alternative to solve these problems is to use smart converters to control the reactive power to regulate the voltage. Volt-Var control (VVC) is one of the simplest approaches for controlling the reactive power from smart converters. Among several converters, grid forming converters (GFCs) are more suitable in DER-enriched distribution networks. Since DER-enriched distribution networks have a higher fluctuation in voltage profile, real-time control is advantageous. Therefore, this work presents an advanced real-time reactive power control for handling voltage violations in a DN using GFC. The uniqueness of this method is that it controls the voltage magnitude of affected nodes by dispatching reactive power from smart converters in real-time. By running cyber-physical co-simulation (CPCS) between the Typhoon HIL 604 and OpenDSS, the Volt-Var control can be done in real time. The grid-forming converter is modelled in Typhoon HIL 604, which acts as a physical layer of the proposed cyber-physical system for real-time VVC. A CIGRE medium voltage distribution network is designed in OpenDSS and serves as one of the parts of the cyber layer. The CPCS between Typhoon HIL and OpenDSS and the control algorithm are both done by a programme written in Python. The execution of the control algorithm is performed in real time using the Supervisory Control and Data Acquisition (SCADA) developed in this study. The real-time simulation shows that the proposed real-time VVC is capable of handling voltage violations in real time in DER-enriched distribution networks.

Optimal design of dual air-gap closed-loop TMR current sensor based on minimum magnetic field uniformity coefficient

Advanced sensor technology provides accurate information for transparent monitoring and real-time control of the power grid. Tunnel magnetoresistance (TMR) elements with high sensitivity and linearity provide a new technical means for current measurement in medium-voltage DC power distribution systems. This paper proposes a dual air-gap closed-loop TMR current sensor and its optimal design method based on the magnetic field’s minimum uniformity coefficient. The dual air-gap structure reduces the measurement error caused by the eccentricity of the wire, and the theory and modelling of the minimum magnetic field uniformity coefficient optimise the key parameters, such as the inner radius of the magnetic core, the distance of the air-gap and the area size of the section side. Finally, a sensor prototype with a rated measurement current of ± 50 A was developed. The experiment results show that the relative error of the proposed TMR current sensor is less than 0.2% under the rated current. The conclusion can be drawn that the proposed sensor with the optimised design effectively improves the measurement accuracy.

Noise-Immune Machine Learning and Autonomous Grid Control

Most recently, stochastic control methods such as deep reinforcement learning (DRL) have proven to be efficient and quick converging methods in providing localized grid voltage control. Because of the random dynamical characteristics of grid reactive loads and bus voltages, such stochastic control methods are particularly useful in accurately predicting future voltage levels and in minimizing associated cost functions. Although DRL is capable of quickly inferring future voltage levels given specific voltage control actions, it is prone to high variance when the learning rate or discount factors are set for rapid convergence in the presence of bus noise. Evolutionary learning is also capable of minimizing cost function and can be leveraged for localized grid control, but it does not infer future voltage levels given specific control inputs and instead simply selects those control actions that result in the best voltage control. For this reason, evolutionary learning is better suited than DRL for voltage control in noisy grid environments. To illustrate this, using a cyber adversary to inject random noise, we compare the use of evolutionary learning and DRL in autonomous voltage control (AVC) under noisy control conditions and show that it is possible to achieve a high mean voltage control using a genetic algorithm (GA). We show that the GA additionally can provide superior AVC to DRL with comparable computational efficiency. We illustrate that the superior noise immunity properties of evolutionary learning make it a good choice for implementing AVC in noisy environments or in the presence of random cyber-attacks.

Evaluation of optimisation techniques on the performance of an on-line MPC controller in an occupancy grid for autonomous mobile robots

Computational capabilities on embedded platforms, especially for autonomous mobile robotics, enable them to be used for more challenging tasks, e.g., to apply advanced control methods like an on-line MPC controller on the robot itself. In the last decades, many improvements in direct methods to solve the optimal control problem were proposed, which speed up the optimisation process. We apply the multiple shooting approach to solve the Optimal Control Problem in a parallel manner. We apply the on-line MPC scheme with the multiple shooting approach in simulations, assess them in a hardware-in-the-loop scenario and conduct experiments on a Raspberry Pi 4-based differential-drive-platform to assess the realtime capability to control the robot while the optimisation is carried out locally.

SWRDFT Controller for Single Phase Grid Tied PV system with Low Voltage Ride through Capability

Voltage control and power quality (PQ) management are crucial aspects for the control of grid connected voltage source converters (VSC). In this paper, a novel control technique for solar photovoltaic (PV) tied single-phase grid is described based on a Sliding Window Recursive Discrete Fourier Transform (SWRDFT). This article considers a single-phase, double-staged PV interconnection. The major goal of control is to operate the VSC as per the standard grid codes and ensure quality power is delivered to the grid even under abnormal grid conditions. The system is designed to work at unity power factor during typical operation. Reactive power injection is mandated as per a country's grid code and helps to regulate the voltage profile and low voltage ride through (LVRT) during grid voltage fluctuations. Furthermore, the paper focuses on two important aspects viz. load compensation and LVRT operation carried out in the event of grid voltage sags. During night time operation, in the absence of PV, the VSC and dc link capacitor operate as a shunt compensator. The proposed controller completes both the tasks as demonstrated by simulation and hardware results. Additionally, a comparison study using state of the art fundamental extraction techniques based on Second / Third order generalized Integrator (SOGI/TOGI) is presented.

Unrolled Spatiotemporal Graph Convolutional Network for Distribution System State Estimation and Forecasting

Timely perception of distribution system states is critical for the control and operation of power grids. Recently, it has been seriously challenged by the dramatic voltage fluctuations induced by high renewables. To address this issue, an Unrolled Spatiotemporal Graph Convolutional Network (USGCN) is proposed for distribution system state estimation (DSSE) and forecasting with augmented consideration of the underlying complex spatiotemporal correlations of renewable energy sources (RES). Specifically, the interconnection among individual spatial graphs of adjacent time steps will lead to an unrolled spatiotemporal graph and benefit the synchronous capture of spatial and temporal correlations to achieve enhanced accuracy. On top of this, the node-embedding technique is employed in the unrolled spatiotemporal convolutional layer to reveal the hidden nonlinear spatiotemporal correlations of RES outputs without relying on full prior knowledge. Moreover, the proposed USGCN stacks the unrolled spatiotemporal convolutional layers, leading to the perception of longtime correlations to obtain effective ahead-of-time state forecasting results robustly. The simulation results have been provided to verify the accuracy and efficiency of the proposed model in 118-node and 1746-node distribution systems.

Automatic Voltage Control of Differential Power Grids Based on Transfer Learning and Deep Reinforcement Learning

Automatic Voltage Control of Differential Power Grids Based on Transfer Learning and Deep Reinforcement Learning

Energy Management, Control, and Operations in Smart Grids: Leveraging Blockchain Technology for Enhanced Solutions

As smart grids advance rapidly, they are evolving along two primary trajectories: (1) digitalization through the incorporation of Internet of Things (IoT) technology and intelligent control, and (2) decentralization by leveraging small-scale distributed energy sources for control. However, these developments also introduce complexities in the functioning, management, and control of smart grids. Consequently, there is an urgent need for a transparent, secure, robust, transactive, and scalable framework for all stakeholders and operators. Blockchain technology emerges as a promising solution for this new smart grid paradigm, providing a range of features, including a distributed ledger, immutability, consensus, security, and automation. This article highlights the challenges in smart grid management, control, and operations, and discusses the potential of blockchain-based solutions to address these issues. It demonstrates that the adoption of blockchain can significantly enhance the overall functioning of the infrastructure by establishing a decentralized architecture without central governance.

A Consensus-Based Distributed Temperature Priority Control of Air Conditioner Clusters for Voltage Regulation in Distribution Networks

High penetration of Photovoltaic (PV) to the distribution network may bring under-voltage and over-voltage issues, limiting the PV hosting capacity. Air conditioners (AC) in grid-interactive buildings can support voltage regulation by manipulating flexible energy consumption. This paper developed a novel voltage control strategy to regulate the AC clusters’ on/off states for distribution network voltage regulation under high PV penetrations. The novelty lies in the distributed formulation of temperature priority-based on/off control (TPC) of AC clusters and the strategic selection and permutation of demand response technologies, including the real-time optimal demand response resources dispatch, distributed sensing of ACs based on average consensus algorithm, and the local implementation of TPC strategy and trial calculation scheme for flexibility capacity estimation. Finally, the distributed TPC is validated to be effective for system rebalancing with no comfort violations and an acceptable ON/OFF switching frequency. The theoretical and numerical analysis also proves its scalability and robustness to communication delays and link failures. It is then incorporated into a novel hierarchical control framework for smart grid voltage control in a four-bus three-phase test grid, considering the voltage sensitivities to power injections in different locations and phases.

An Innovative Converterless Solar PV Control Strategy for a Grid Connected Hybrid PV/Wind/Fuel-Cell System Coupled With Battery Energy Storage

The proposed work addresses the modeling, control, energy management and operation of hybrid grid connected system with wind-PV-Battery Energy Storage System (BESS) integrated with Fuel Cell (FC) and Electrolyzer. A hybrid PV-Wind-FC with electrolyzer consisting of BESS with the least number of control loops and converters has been proposed. The proposed hybrid system presents a cost efficient solution for integrating PV into a hybrid system by eliminating the PV converter. This includes the design of controllers for grid-connected hybrid systems with a renewable distributed generator (Wind and PV) as a primary source, BESS as a secondary source and FC with Electrolyzer as a tertiary source. In addition, the lead compensator along with integrator is used for obtaining enough phase margin and removing steady state error completely. It increases the stability of the controller and adds phase shift φ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>s</i></sub> at a cross gain frequency (ω <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>cut</i></sub> ). The Grid Side Controller (GSC) is capable of providing frequency support to the utility grid, when it is linked to the grid. In the proposed configuration, PV power is maximized and injected into grid through GSC. Rotor Side Converter (RSC) and GSC ensure the support for sharing the burden of the grid station. Moreover, the proposed controller of BESS with coordination of FC eliminates the effect of intermittency of power generated from wind and PV. Excess power production by renewable distribution generation is used by Electrolyzer to generate hydrogen. This hydrogen is further used by FC when there is not enough power generation due to unfavorable weather conditions. The energy management has been presented to fulfill the load profile, avoid BESS overcharging and to minimize the intermittency and fluctuation of Wind and PV sources. This method guarantees steady power flow and service continuity. The Simulink model of the proposed system results validate the efficiency of the proposed hybrid system as compared to the conventional hybrid system reported in the literature. The modeling of the proposed system and analysis has been demonstrated using the MATLAB Simulink model. Lastly, the energy management of the system has also been examined and compared with the conventional power system.

Robust Optimized Current Controller Based on a Two-Step Procedure for Grid-Connected Converters

Proportional-resonant controllers are largely used for grid-connected converters with LCL filters to ensure sinusoidal reference tracking and harmonic disturbance rejection. However, the design of such controllers becomes more difficult when it is necessary to ensure stability and suitable performance for converters operating under uncertain grid conditions, from stiff to weaker, and also when the number of resonants increases. In this more challenging scenario, available design procedures may lead to a poor tradeoff between transient and steady-state performances. To overcome this problem, this paper proposes an off-line automated control design procedure to obtain multiple robust optimized proportional-resonant controllers. The proposed procedure consists of two steps, where a particle swarm algorithm is used to obtain: i) the gains of an inner loop ensuring optimal active damping for the LCL filter resonance; ii) the gains of multiple proportional-resonant controllers by minimizing a tracking error index, taking into account uncertain grid impedance and control signal limits. The robust stability of the closed-loop system with the control gains tuned by the metaheuristics is certificated through a parameter-dependent Lyapunov function, constructed based on linear matrix inequalities. Several results from control Hardware-in-the-loop are presented for a case study, revealing that the proposal provides better performance in terms of reference tracking and disturbance rejection, when compared to similar control strategies from the literature. Experimental results from a prototype connected to the utility grid attest the practical feasibility of the proposed procedure.