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Abstract The uncertainty brought by the integration of distributed generations in distribution networks poses a higher demand for situation awareness in the distribution network. Accurate identification of distribution network line parameters is of great significance for the operation and control of the distribution network. This paper proposes a method for identifying distribution network line parameters considering multisource measurement. Firstly, the initial values of conductivity and susceptance are obtained through linear regression and converted into resistance and reactance, respectively. Then, based on the series parallel connection of the network end branches, a non‐linear function about resistance reactance is derived. By combining the measurement data of micro phasor measurement unit and advanced metering infrastructure at multiple times, the non‐linear measurement equation of the line is established, and the Levenberg–Marquardt algorithm is used to solve the non‐linear function, thus achieving the identification of distribution line parameters. The case study demonstrates the accuracy and effectiveness of the proposed method.

The coordinated development of offshore wind and electricity–hydrogen systems (OW-EHS) has the potential to enhance the utilization of renewable energy sources and achieve carbon neutrality. This article presents a case study of nine coastal provinces in China and designs four scenarios to analyze the economic benefits and potential hydrogen production capacity of OW-EHS. A multi-stage stochastic dual dynamic programming based approach is proposed for integrating OW-EHS, exploring offshore wind energy as both a primary electricity source for coastal regions and a clean, sustainable energy source for electrolyzers. It accounts for variations in hydrogen demand, wind uncertainty, load profiles, and electricity price dynamics, offering a holistic perspective on the feasibility and benefits of collaborative offshore wind and electricity–hydrogen systems. A comparative analysis highlights the effectiveness of the proposed planning approach, demonstrating its capacity to maximize the overall system benefits, encompassing both the optimal levelized cost of energy and hydrogen production. The new discoveries elucidate the unique attributes and advantages of each coastal province in OW-EHS. This research provides a tailored blueprint for coastal provinces in China to harness their offshore wind potential and accelerate progress towards carbon neutrality.

This paper investigates a novel adaptive voltage control over a three-phase grid-forming (GFM) inverter. The proposed voltage controller includes two function parts: power control input and signal control input. The former improves dynamic performance by taking advantage of state feedback, load current feedforward, and reference feedforward, while the latter, a time-variant signal in reference feedforward, considers uncertainties of system parameters and load disturbances. In order to compensate for harmonic distortion on loads, a multi-resonant control is added to the signal control input. Then, a multi-resonant based reference feedforward adaptive control strategy is put forward. Thanks to its linearization, the stability of the strategy is ensured by the mature linear control theory instead of the Lyapunov stability theory. Then, the principles of parameter selection based on the root locus method are introduced to improve the dynamic performance of this technique. Compared with classical PI/PR methods or other adaptive ones, this newly proposed adaptive control method synthesizes high robustness, excellent voltage regulation performance, and low total harmonic distortion under nonlinear loads in island mode. The experiments verify its effectiveness on a GFM inverter using a TI TMS320F28335 DSP.

Modern large-scale active distribution networks have complex and dynamic operating circumstances, which pose major difficulties to state estimation (SE) technology. This study suggests a novel forecasting aided state estimate (FASE) framework based on an enhanced pseudo measurement modeling approach to overcome these issues and boost management and control decisions. Specifically, the suggested framework employs an advanced kind of pseudo measurement modeling that builds a model that is consistent with the distribution network’s real operation by using a support vector machine (SVM) with an upgraded kernel function. Furthermore, we introduce a numerical stability enhanced FASE algorithm that enhances the accuracy and efficiency of the estimation process. Through the application of measurement transformation and trustworthy pseudo measurement data as input, the FASE algorithm attains high-precision operational parameter awareness of the distribution network. Ultimately, the case study illustrates the benefits of the suggested framework over existing methods in terms of estimation accuracy, efficiency, and numerical stability compared to existing methods.

Extensive penetration of distribution energy resources (DERs) brings increasing uncertainties to distribution networks. Accurate topology identification is a critical basis to guarantee robust distribution network operation. Many algorithms that estimate distribution network topology have already been employed. Unfortunately, most are based on data-driven alone method and are hard to deal with ever-changing distribution network physical structures. Under these backgrounds, this paper proposes a data-model hybrid driven topology identification scheme for distribution networks. First, a data-driven method based on a deep belief network (DBN) and random forest (RF) algorithm is used to realize the distribution network topology rough identification. Then, the rough identification results in the previous step are used to make a model of distribution network topology. The model transforms the topology identification problem into a mixed integer programming problem to correct the rough topology further. Performance of the proposed method is verified in an IEEE 33-bus test system and modified 292-bus system.

A rational partition is the key prerequisite for the application of distributed algorithms in distribution networks. This paper proposes community-detection-based approaches to a distribution network partition, including a non-overlapping partition and a border-node partitioning method. First, a novel electrical distance is defined to quantify the coupling relationships between buses and it is further used as the edge weight in a transformed equivalent graph. Then, a vertex/link partition community detection approach is applied to over-partition the network into high intra-cohesive and low inter-coupled subregions. Following this, a greedy algorithm and a tabu search method are combined to merge these subregions into target numbers according to the scale similarity principle. The proposed approaches take the influence of three-phase imbalance into consideration and they are decoupled from the power flow. Finally, the approaches are tested on an IEEE 123-bus distribution system and the results verify the effectiveness and the credibility of our proposed methods.

This paper proposes a distributed data-driven distributionally robust volt/var control (DDDR-VVC) approach which schedules on-load-tap changer (OLTC), capacitor banks (CBs) and Photovoltaic (PV) inverters coordinately. With integration of distributed optimization and data-driven distributionally robust optimization, the DDDR-VVC model is constructed to reduce power losses and maintain voltage within allowable range under uncertainty. To fully address the uncertainty impacts, construction of uncertain set is improved through that the probability distribution sets and trapezoidal fuzzy functions are developed to model PV supply and load demand respectively. An accelerated alternating optimization procedure which integrates the alternating direction method of multipliers and the column-and-constraint generation algorithm is proposed. This method improves the dual information update via Nesterov’s accelerated gradient method, thus solving the DDDR-VVC model at a high convergence rate. The effectiveness of proposed method is verified through numerical simulations using IEEE 123 bus test system.

Abstract Long‐term thermal ageing might have impacts on the physicochemical properties of cross‐linked polyethene (XLPE) cables, thereby influencing their electrical behaviours, such as electrical treeing initiation and growth. The main objective of this work is to report the effects of thermal ageing on the electrical treeing behaviours (initiation and growth) in XLPE cable insulation. For this purpose, the electrical treeing initiation and growth test were performed in accelerated thermal samples. In addition, Differential Scanning Calorimetry and Fourier Transform Infrared Spectroscopy were utilised to investigate corresponding changes in the microstructures of materials. In addition, a correlation was established between electrical treeing behaviours and physicochemical properties during the thermal ageing process. Test results indicate that the thermal ageing process should be divided into three phases, and the re‐crystallisation reaction is considered the primary reason for the increase of average electrical tree initiation voltage (AETIV). Furthermore, the thermal ageing index Xc is correlated to AETIV and the propagation rate of the electrical tree. As equivalent relationships have been established between the relative decrease value w of indexes AETIV, the propagating rate and Xc, this method can be employed to predict the changing pattern of electrical treeing initiation voltage and the propagating rate in future research.

Due to the problems such as fuzzy state assessment grading boundaries, the recognition accuracy is low when using traditional fuzzy techniques to grade the switchgear state. To address this problem, this paper proposes a switchgear state assessment and grading method based on deep belief network (DBN) and improved fuzzy C-means clustering (IFCM). Firstly, the switchgear state information data are processed by normalization method; then the feature parameters are extracted from the switchgear state information data by using DBN, and finally the extracted feature parameters are categorised according to the condition of switchgear equipment through clustering using IFCM. The experimental results show that the accuracy of the method in assessing the switchgear state under small sample conditions reaches 94, which exceeds the accuracy of other switchgear state assessment grading methods currently in use.

Photovoltaic (PV) power generation has highly penetrated in distribution networks, providing clean and sustainable energy. However, its uncertain and intermittent power outputs significantly impair network operation, leading to unexpected power loss and voltage fluctuation. To address the uncertainties, this paper proposes a multi-timescale affinely adjustable robust reactive power dispatch (MTAAR-RPD) method to reduce the network power losses as well as alleviate voltage deviations and fluctuations. The MTAAR-RPD aims to coordinate on-load tap changers (OLTCs), capacitor banks (CBs), and PV inverters through a three-stage structure which covers multiple timescales of “hour-minute-second”. The first stage schedules CBs and OLTCs hourly while the second stage dispatches the base reactive power outputs of PV inverter every 15 min. The third stage affinely adjusts the inverter reactive power output based on an optimized Q-P droop controller in real time. The three stages are coordinately optimized by an affinely adjustable robust optimization method. A solution algorithm based on a cutting plane algorithm is developed to solve the optimization problem effectively. The proposed method is verified through theoretical analysis and numerical simulations.

Due to the influence of electromagnetic noise and other factors, existing cable partial discharge location methods cannot accurately locate discharge faults. To handle it, this paper proposes an online localization method for cable partial discharge signals that is suitable for practical engineering applications, based on the double-end localization method. The proposed method uses an improved generalized correlation algorithm to estimate the signal time delay, and takes into account the wave speed uncertainty of the local discharge signal. To improve local localizing accuracy under multiple localizing samples, a trimmed-mean data filtering speed algorithm is employed. Simulation and experimental results demonstrate that the proposed method effectively enhances time delay estimation accuracy and reduces localization error, even under complex electromagnetic noise environment and restricted sampling rates of detection equipment, when compared to traditional time delay estimation localization methods. The positioning accuracy of partial discharge in field experiments reached 97.43%-99.89%, which meets actual engineering requirements.

The accurate estimation of the state-of-charge (SOC) and state-of-health (SOH) of lithium-ion batteries is crucial for the safe and reliable operation of battery systems. In order to overcome the practical problems of low accuracy, slow convergence and insufficient robustness in the existing joint estimation algorithms of SOC and SOH, a Dual Adaptive Central Difference H-Infinity Filter algorithm is proposed. Firstly, the Forgetting Factor Recursive Least Squares (FFRLS) algorithm is employed for parameter identification, and an inner loop with multiple updates of the parameter estimation vector is added to improve the accuracy of parameter identification. Secondly, the capacity is selected as the characterization of SOH, and the open circuit voltage and capacity are used as the state variables for capacity estimation to improve its convergence speed. Meanwhile, considering the interaction between SOC and SOH, the state space equations of SOC and SOH estimation are established. Moreover, the proposed algorithm introduces a robust discrete H-infinity filter equation to improve the measurement update on the basis of the central differential Kalman filter with good accuracy, and combines the Sage–Husa adaptive filter to achieve the joint estimation of SOC and SOH. Finally, under Urban Dynamometer Driving Schedule (UDDS) and Highway Fuel Economy Test (HWFET) conditions, the SOC estimation errors are 0.5% and 0.63%, and the SOH maximum estimation errors are 0.73% and 0.86%, indicating that the proposed algorithm has higher accuracy compared to the traditional algorithm. The experimental results at different initial values of capacity and SOC demonstrate that the proposed algorithm showcases enhanced convergence speed and robustness.

Synchronous rectification (SR) technology has been a critical technology for LLC converters to achieve high efficiency and power density. However, conventional SR driving methods face challenges in terms of light-load condition, module size, switching accuracy, and circuit complexity. This paper proposes an SR driving strategy based on resonant inductor voltage (RLV) to address those issues. This RLV-SR driving strategy does not require current sensors and is insensitive to rectifier parasitic parameters. In addition, the RLV-SR driving strategy can be applied in a relatively wide operating frequency range and load conditions. Experimental results based on a 100-W/24-V LLC converter are presented to verify the effectiveness of the proposed RLV-SR driving strategy. Furthermore, the error of turn-on time caused by stray inductance is significantly reduced compared with the conventional VDS-ON sensing method, which improves the power converter’s efficiency.

Abstract The increasingly close connections between multiple heterogeneous energy subsystems, the integrated energy system (IES) will play a critical role in ensuring future energy generations and distributions. As an essential function of the energy management system, state estimation provides data support for energy management of IES. Unfortunately, the existing IES state estimation method does not consider the measurement delay, which is inconsistent with the measurement delay characteristics caused by the multi‐timescale characteristics of IES. Driven by this motivation, this paper proposes a real‐time state estimation framework for the gas‐electricity coupled system. Considering the characteristics of the gas pipelines and coupling elements, the dynamic model of the natural gas system is established. A dynamic state estimation algorithm to enhance numerical stability is adopted to solve the problem that real‐time estimation based on the traditional Kalman filter suffers from the curse of dimensionality. Finally, a modified unscented Kalman filter (UKF) based estimation method is designed based on unified time processing and delay noise synthesizing. The IEEE 39‐bus electrical system and the 20‐node Belgian gas system are coupled to form the test system in this paper. The case study shows the advantages of the proposed method in efficiency and accuracy compared with the existing methods.

Introduction: This paper proposes a sigmoid-based adaptive inertia control strategy for grid-forming (GFM) inverter to enhance frequency stability.Methods: Firstly, the frequency response characteristics under different disturbances are analyzed theoretically. Then, to solve the problem that fixed inertia leads to a contradiction between the maximum frequency deviation and the setting time, a non-linear inertia regulator is investigated. This non-linear inertia regulator, which is based on an improved sigmoid function, is applied to achieve real-time inertia by the frequency deviation. Moreover, the full-order small-signal model of the system containing the nonlinear inertia regulator is established and thus the stability of the proposed strategy is analyzed.Result and Discussion: The proposed control strategy is implemented without derivative action, which may suffer from high-frequency noises. Finally, the hardware-inthe-loop (HIL) results verify the remarkable performance of the proposed control strategy.

Abstract Cross‐linked polyethylene (XLPE) cables are a critical component of power transmission systems. As operating conditions sometimes exceed the thresholds set in discipline, some cables fail to reach their designed lifespan, and consequently evaluating the aging degree of cables is particularly necessary. Here ten cables with an operating duration between 1 and 11 years from adjacent lines were sampled. Investigations were performed through various aging evaluation methods. Then typical lifespan prediction indices were concluded from test results and filtrated through correlation calculation. Meanwhile, a lifespan prediction method based on GM (1, N) model was proposed to establish the equivalent relationship between operating time and these indices. The research results indicated that, lifespan prediction indices obtained were all significantly correlated with operating time, which can be used to characterize aging degree of XLPE cables. Besides, due to the high prediction accuracy of experimental verification, the GM (1, N) model was proved to be applicable to evaluate the aging degree quantitatively and determine the remaining lifespan (relative to the designed lifespan) of XLPE cables. In future research, this method can be used to formulate a reasonable and economic withdrawal strategy for XLPE cables operating in conditions that exceed the thresholds set in the discipline.

As the penetration of renewable energy resources increases, their intrinsic uncertainty and randomness raise significant concerns about the grid’s reliability. With the development of information technologies, residential air-conditioners serve increasing services, including secondary frequency regulation, peak load shaving, and capacity reserve. However, these resources can hardly be allocated economically and reliably. On the one hand, it is unrealistic to provide advanced and expensive control modes for all AC units due to the limited investment cost. On the other hand, the uncertainty of residents’ behavior varying with control modes affects the reliability. Few previous works effectively make a trade-off between economics and reliability, which restricts the value of residential air-conditioners. Thus, this paper proposed an economical and reliable allocation method based on a real survey dataset and the online learning multi-armed bandits framework. This method explores the inherent relationship between the allocation plan’s economics and reliability. We also propose an allocation model which enables system operators to design an economic and reliable plan for each residential area considering ancillary services requirements. A case study in a modified IEEE 9 nodes system illustrates the effectiveness of the proposed method.

As the penetration of distributed renewable generation increases, their intrinsic uncertainty and random raise neutral concerns on the reliability of microgrid. Demand side resources are considered helpful in facilitating the operation of microgrid. In recent years, the development of Internet of Things and electric vehicle enables the capability of residential demands serving as flexible grid assets to provide various ancillary services and enhance operation reliability. For the residential flexible resource, their control modes and subsidy prices greatly affect the performance and operation cost, and different resources, such as air conditioner and electric vehicle (distributed energy storage), have considerably different flexible capability, capacity and quantity. Thus, this paper provides an economic-based resource allocation model for residential flexible resources. The proposed model finds the cost-effective plan about how to allocate residential flexible resources to enhance reliability of microgrid. Case studies are conducted on a modified IEEE 9-node network to illustrate the effectiveness of the proposed model.

Aiming at the stability of a medium-voltage DC network based on a modular multilevel converter (MMC), this paper proposes an admittance stability criterion considering the influence of current-limiting inductors at the medium voltage side, which prevents the complex products and matrix calculations of traditional criteria. The DC admittance model DC transformers (DCTs) under different working modes are then established based on Thevenin/Norton equivalent circuit methods to analyze the stability of the DC system based on the proposed admittance stability criterion, which proves that the voltage resonance problem at the medium voltage side can be improved by adding active damping control strategies on DCTs also proves the effectiveness of the proposed stability criterion. The time-domain simulation and the hardware-in-loop simulation are then built in PLECS and RT Box to further verify the correctness of the system stability analysis and the effectiveness of the proposed admittance criterion, which provides a theoretical basis and technical reserve for the stable operation of the DC distribution power system.

Modular multilevel converters (MMCs), as one of the core components of hybrid AC/DC power grids, become the preferred converter topology and show good developments. Urgently, a general MMC modeling scheme with good model accuracy needs to be developed to realize small-signal analyses and designs for the large-scale AC/DC power grids easily. This paper proposes a unified modeling scheme (UMS) for MMC systems in a synchronous rotating (dq) reference frame. Based on the dynamic phasor theory and with the proposed modular decouple modeling (MDM), the nonlinear state-space model of the overall MMC system can be obtained by configuring and connecting the input and output of the state-space model of each subsystem. Besides, the unified controller, modeling different control modes, normalizes the MMC systems modeling. Simultaneously, with the proposal of UMS, linearization and splicing could be uesd to develop a small-signal model of the overall MMC system directly. Therefore, the proposed model is suitable for simulating the large-scale hybrid AC/DC power grids and analyzing the stability of small-signal. Finally, the simulation results verify the accuracy and effectiveness of the proposed modeling method.

The concept of virtual synchronous machine (VSM) was proposed to deal with the shortcomings of low inertia and damping of traditional control strategies for power electronic converters. But what if all distributed energy resources and controllable loads in a microgrid adopt the VSM control strategy, and will it present better performance than conventional droop control-based microgrid (DMG)? In this paper, the VSM-based microgrid (VSMG) is analyzed. The small-signal modeling of the VSMG is studied at first. Then static stability and dynamic characteristics of the VSMG are analyzed and compared with the DMG in both frequency-domain and time-domain. With the growing scale of microgrids, their modeling and simulation are becoming significant computational burdens. Inspired by the participation factor analysis of the VSMG and the concept of coherency in power systems, the VSMG small-signal model is equivalent to a modified third-order synchronous generator (SG) model in this paper. The equivalencing involves gray-box system identification and is realized by estimating equivalent electrical parameters alternately and iteratively. The equivalent SG (EqSG) model is compared with three representative model order reduction methods to verify its effectiveness. Simulation results confirm the accuracy of the EqSG model substituting detailed VSMG model in time-domain simulations.

In order to avoid overuse of a certain battery energy storage system (BESS) and prolong the cycle life of battery in AC microgrid, an improved SoC-based droop control based on multi-agent system (MAS) is proposed for achieving State of Charge (SoC) balance of multiple BESS units. A proportional-integral (PI) adjustment item using the average SoC is added to the P -f droop to regulate the charging and discharging power of BESS, then SoC changes towards in the direction of consistency and finally reaches equalization. The dynamic average consensus algorithm is utilized to obtain the average SoC. The proposed SoC-based droop method needs not to be changed in different operating mode of BESS and can achieve SoC balance regardless of whether the capacities of different batteries are the same, which improve the applicability of this method. A complete small-signal state space model including all BESS units is built and analyzed to select the appropriate control parameters of the improved droop. The impact of communication time delay on the improved droop method for balancing SoC is investigated and the effectiveness of the proposed strategy is verified through the simulation results of different case studies.

As the penetration of renewable energy continues to increase, stochastic and intermittent generation resources gradually replace the conventional generators, bringing significant challenges in stabilizing power system frequency. Thus, aggregating demand-side resources for frequency regulation attracts attentions from both academia and industry. However, in practice, conventional aggregation approaches suffer from random and uncertain behaviors of the users such as opting out control signals. The risk-averse multi-armed bandit learning approach is adopted to learn the behaviors of the users and a novel aggregation strategy is developed for residential heating, ventilation, and air conditioning (HVAC) to provide reliable secondary frequency regulation. Compared with the conventional approach, the simulation results show that the risk-averse multi-armed bandit learning approach performs better in secondary frequency regulation with fewer users being selected and opting out of the control. Besides, the proposed approach is more robust to random and changing behaviors of the users.

Future power distribution networks are characterized by changes of uncertain behavior due to the integration of distributed generators (DG) and prosumers, and the design of meter deployment is more critical for the safe and effective network monitoring system. The use of any available and the newly installed metering devices such as micro phasor measurement units (μPMU) and smart meters (SM), could offer much accurate and real-time measuring data. This paper investigates a novel meter deployment method for the distribution network planning, with the objective of ensuring the quality of the state estimation (SE) under consideration of measurement uncertainty, while keeping the total amount of heterogeneous meters at a minimum. In particular, the measurement uncertainty in the SE is depicted via the interval arithmetic (IA) theory, which could offer advantageous over the existing probabilistic and fuzzy approaches. The case studies and comparison results based on an actual distribution network demonstrate the feasibility of the proposed IA-based SE algorithm and the meter deployment method.

This paper proposes a two-stage decentralized optimal reactive power dispatch (D-ORPD) framework, which considers the network partitioning influence in distributed optimization. The first stage is to divide the distribution networks (DNs) into several high-intra-cohesion and low-coupling sub-networks while the mathematical model is constructed based on partition indexes. This optimal partition designation reduces information exchange between adjacent sub-networks. Then, with the reduced information exchange, the distributed computation would be accelerated. Based on the network optimal partition, the D-ORPD model is constructed in a “decomposition-coordination”pattern during the second stage. To enhance the consistency between distributed and centralized optimization, the D-ORPD is improved by the proposed virtual load, which simulates the load characteristic of sub-networks. The alternating directions method of the multipliers (ADMM) algorithm is utilized to solve the mathematical model effectively. Moreover, case studies on the PG&E 69 bus test system and the IEEE 123 bus test system are executed by the MATLAB platform to demonstrate the validity and effectiveness of the proposed method.

This paper presents a novel distributed voltage control strategy to maintain the voltage of active distribution networks containing multiple microgrids. Local voltage regulation characteristics, such as power reserve, adjustment cost, and regulating speed, are identified first. According to a neighbors' voltage regulation characteristics, the microgrid coordinates neighborhood resources and asks them for coordination as soon as voltage problems occur. Microgrids schedule local resources based on a cooperative distributed model predictive control algorithm scheme with neighbor-to-neighbor communication. Tests are performed in an active distribution network with three microgrids. The numerical studies have shown that the proposed strategy mitigates the voltage fluctuation rapidly and efficiently with minimum operating cost.

In this paper, cross-linked polyethylene (XLPE) cables of the same batch from Factory A, which ran from 1 to 8 years in Jiangsu Province, are sampled. Some widely accepted aging characterization methods of XLPE cables such as the gel content test, differential scanning calorimetry (DSC) test, tensile test and hardness test are employed to obtain the physicochemical, mechanical and electrical properties of the samples. Then, some lifespan prediction parameters significantly correlated with operating time are obtained through correlation calculations. Finally, a prediction method is proposed to predict the operating time of XLPE cables from Factory A. The test results indicate that parameters including the gel content Cge, the crystallinity XC, tensile strength σ, ultimate elongation δ, the dielectric permittivity ε, and the dielectric loss Jtan are significantly correlated with operating time, which can be used in evaluating the aging degree of XLPE cables. Moreover, due to the high accuracy of the experimental verification, it turns out that the lifespan prediction method proposed in this paper can be used to determine the operating time of XLPE cables from Factory A in future research.

The Voltage Source Converter-HVDC (VSC-HVDC) system applied to wind power generation can solve large scale wind farm grid-connection and long distance transmission problems. However, the low voltage ride through (LVRT) of the VSC-HVDC connected wind farm is a key technology issue that must be solved, and it is currently lacking an economic and effective solution. In this paper, a LVRT coordinated control strategy is proposed for the VSC-HVDC-based wind power system. In this strategy, the operation and control of VSC-HVDC and wind farm during the grid fault period is improved. The VSC-HVDC system not only provides reactive power support to the grid, but also effectively maintains the power balance and DC voltage stability by reducing wind-farm power output, without increasing the equipment investment. Correspondingly, to eliminate the influence on permanent magnet synchronous generator (PMSG)-based wind turbine (WT) systems, a hierarchical control strategy is designed. The speed and validity of the proposed LVRT coordinated control strategy and hierarchical control strategy were verified by MATLAB/Simulink simulations.

Inverter voltage control is an important task in the operation of a DC/AC microgrid system. To improve the inverter voltage control dynamics, traditional approaches attempt to measure and feedforward the load current, which, however, needs remote measurement with communications in a microgrid system with distributed loads. In this paper, a load current observer (LCO) based control strategy, which does not need remote measurement, is proposed for sinusoidal signals tracking control of a three-phase inverter of the microgrid. With LCO, the load current is estimated precisely, acting as the feedforward of the dual-loop control, which can effectively enlarge the stability margin of the control system and improve the dynamic response to load disturbance. Furthermore, multiple PR regulators are applied in this strategy conducted in a stationary frame to suppress the transient fluctuations and the total harmonic distortion (THD) of the output voltage and achieve faster transient performance compared with traditional dual-loop control in a rotating dq0 frame under instantaneous change of various types of load (i.e., balanced load, unbalanced load, and nonlinear load). The parameters of multiple PR regulators are analyzed and selected through the root locus method and the stability of the whole control system is evaluated and analyzed. Finally, the validity of the proposed approach is verified through simulations and a three-phase prototype test system with a TMS320F28335 DSP.

In this paper, a distributed economic dispatch scheme considering power limit is proposed to minimize the total active power generation cost in a droop-based autonomous direct current (DC) microgrid. The economical dispatch of the microgrid is realized through a fully distributed hierarchical control. In the tertiary level, an incremental cost consensus-based algorithm embedded into the economical regulator is utilized to search for the optimal solution. In the secondary level, the voltage regulator estimating the average voltage of the DC microgrid is used to generate the voltage correction item and eliminate the power and voltage oscillation caused by the deviation between different items. Then, the droop controller in the primary level receives the reference values from the upper level to ensure the output power converging to the optimum while recovering the average voltage of the system. Further, the dynamic model is established and the optimal communication network topology minimizing the impact of time delay on the voltage estimation is given in this paper. Finally, a low-voltage DC microgrid simulation platform containing different types of distributed generators is built, and the effectiveness of the proposed scheme and the performance of the optimal topology are verified.

Operation control is a vital and complex issue for microgrids. The objective of this paper is to explore the practical means of applying decentralized control by using a multi agent system in actual microgrids and devices. This paper presents a hierarchical control framework (HCF) consisting of local reaction control (LRC) level, local decision control (LDC) level, horizontal cooperation control (HCC) level and vertical cooperation control (VCC) level to meet different control requirements of a microgrid. Then, a hybrid multi-agent control model (HAM) is proposed to implement HCF, and the properties, functionalities and operating rules of HAM are described. Furthermore, the paper elaborates on the implementation of HAM based on the IEC 61850 Standard, and proposes some new implementation methods, such as extended information models of IEC 61850 with agent communication language and bidirectional interaction mechanism of generic object oriented substation event (GOOSE) communication. A hardware design and software system are proposed and the results of simulation and laboratory tests verify the effectiveness of the proposed strategies, models and implementations.

Ultracapacitors (UCs), with their features of high power density and high current charge-discharge, have become the best choice for dynamic power compensation to improve the stability of microgrids and are increasingly being applied in microgrids. This paper presents the control of an energy storage system (ESS) based on ultracapacitors in the context of grid-connected microgrids. The ESS is composed of DC/AC and DC/DC converters tied by a dc link. An improved dynamic model for the ESS is proposed. Based on the proposed model a Proportional-Integral-Resonant (PIR) DC link voltage controller is proposed to maintain the DC link voltage through the charging-discharging control of ultracapacitors, capable of working properly under all operating conditions. An extra double frequency component is injected into the UC current by a R controller to dynamically compensate for DC instantaneous power and double frequency AC instantaneous power due to unbalanced grid conditions and disturbances. This feature maintains the DC link voltage constant under unbalanced conditions and increases the degrees of freedom of the DC/AC converter and thus facilitates the application of UCs in microgrids. Simulation and experimental results verify the effectiveness of the proposed control strategy.

In this paper, based on the similarity, in structure and principle, between a grid-connected converter for a direct-driven permanent magnet synchronous generator (D-PMSG) and an active power filter (APF), a new D-PMSG-based wind turbine (WT) system configuration that includes not only an auxiliary converter in parallel with the grid-side converter, but also a coordinated control strategy, is proposed to enhance the low voltage ride through (LVRT) capability and improve power quality. During normal operation, the main grid-side converter maintains the DC-link voltage constant, whereas the auxiliary grid-side converter functions as an APF with harmonic suppression and reactive power compensation to improve the power quality. During grid faults, a hierarchical coordinated control scheme for the generator-side converter, main grid-side converter and auxiliary grid-side converter, depending on the grid voltage sags, is presented to enhance the LVRT capability of the direct-driven PMSG WT. The feasibility and the effectiveness of the proposed system’s topology and hierarchical coordinated control strategy were verified using MATLAB/Simulink simulations.

This paper presents an improved control strategy for both the rotor side converter (RSC) and grid side converter (GSC) of a doubly fed induction generator (DFIG)-based wind turbine (WT) system to enhance the low voltage ride through (LVRT) capability. Within the proposed control strategy, the RSC control introduces transient feed-forward compensation terms to mitigate the high frequency harmonic components and reduce the surge in the rotor currents. The proposed GSC control scheme also introduces a compensation term reflecting the instantaneous variation of the output power of the rotor side converter with consideration of the instantaneous power of grid filter impendence to keep the dc-link voltage nearly constant during the grid faults. To provide precise control, non-ideal proportional resonant (PR) controllers for both the RSC and GSC current regulation are employed to further improve dynamic performance. Simulations performed in Matlab/Simulink verify the effectiveness of the proposed control strategy.

The increasing presence of wind power in power systems will likely drive the integration of large wind farms with electrical networks that are series-compensated to sustain large power flows. This may potentially lead to subsynchronous resonance (SSR) issues. In this paper, a supplementary controller on the grid-side converter (GSC) control loop is designed to mitigate SSR for wind power systems based on doubly fed induction generators (DFIGs) with back-to-back converters. Different supplementary controller feedback signals and modulated-voltage injecting points are proposed and compared based on modal analysis and verified through root locus analysis to identify the optimal feedback signal and the most effective control location for SSR damping. The validity and effectiveness of the proposed supplemental control are demonstrated on the IEEE first benchmark model for computer simulations of SSR by means of time domain simulation analysis using Matlab/Simulink.

To achieve accurate reactive power sharing and voltage frequency and amplitude restoration in low-voltage microgrids, a control strategy combining an improved droop control with distributed secondary power optimization control is proposed. The active and reactive power that each distributed generator (DG) shares is calculated by extracting load information and utilizing a power sharing ratio, and is reset to be the nominal power to recalculate droop gains. The droop control curves are reconstructed according to the nominal active and reactive power and the recalculated droop gains. The reconstructed active power-frequency droop control can regulate active power adaptively and keep frequency at a nominal value. Meanwhile, the reconstructed reactive power voltage droop control can reduce voltage amplitude deviation to a certain extent. A distributed secondary power optimization control is added to the reconstructed reactive power voltage droop control by using average system voltage. The average system voltage is obtained by using a consensus algorithm in a distributed, sparse communication network which is constituted by all controllers of DGs. As a result, accurate reactive power sharing is realized, average system voltage is kept at a nominal value, and all voltage amplitude deviations are further reduced. Due to the absence of a microgrid central controller, the reliability of the strategy is enhanced. Finally, the simulation results validate the proposed method.

A microgrid with low-voltage ride-through capability is designed. The designed microgrid avoids operating in unplanned islanded mode during an asymmetric ground fault which occurs in the low voltage distribution network and supports fault recovery for distribution network. Furthermore, compared with the traditional microgrid topology, the proposed microgrid topology also saves a lot of power electronic devices. The simulation results with PSCAD/EMTDC show that the microgrid can keep the distributed generations and loads operate normally when an asymmetric ground fault occurs in the low voltage distribution grid. It can also increase the active power output according to the requirement of the distribution network to support the distribution network fault recovery.