In this contribution, the three-phase underground cable is modelled using COMSOL Multiphysics software to evaluate the steady state and transient thermal performances. Finite element technique is applied using the heat conduction equation to study the temperature distributions in power cables components and the surrounding environment for both linear and non-linear loads. A real case study of 220 kV, 340 MVA three-phase single core copper cables insulated by XLPE is studied. Other types of insulation such as oil, and SF6 gas and their contributions of convection and radiation are investigated at trefoil and flat configurations. The loading capability under different ambient conditions for average moisture soil and dry soil with low-moisture content are also evaluated taking into account the unfavourable effect of dry zones formation. Moreover, the challenge of predicting the accurate thermal performance and estimating the required derating factor in the presence of odd harmonics is considered. The effect of the change of the frequency spectrum of the non-linear load current by involving different simultaneous harmonic orders for the same total harmonic distortion is extensively investigated for both flat and trefoil configurations. It is concluded that all harmonics contributions should be considered, to accurately calculate the required cable derating.

The study focuses on a novel strategy for microgrid protection based on mathematical morphology. Mathematical morphology is a time-domain signal processing tool that could be used for accurate and reliable signal component extraction. In this study, a class of non-linear multiresolution decomposition scheme called morphological Haar wavelet (MHW) is used for detection of faults in microgrid. The proposed method uses the detail signal of MHW obtained after prediction lifting for fault detection and faulty phase selection. Accordingly, current retrieved from both ends of the feeder are processed through MHW after prediction lifting scheme to obtain the detail signals. The detail signal difference and their norms are calculated to obtain a primary protection scheme for the feeder. Also the same scheme was investigated as backup protection for very low fault resistances by taking the currents of the adjacent feeders. The performance of proposed technique was evaluated for islanded and grid connected mode of operation, for different network topologies, i.e. radial and looped system for various fault and non-fault disturbances.

The increasing penetration of wind power (WP) and demand response (DR) programs into modern power systems poses more challenges on transmission expansion planning (TEP). To ensure the economical, secure and reliable operations of power systems, this study presents a risk-averse TEP framework. Instead of using the deterministic security criterion, an insecurity risk cost (RC) is proposed to provide network planners with the insight into the problem, options and future implications in decision making. Specifically, this RC can quantify the system security degree, considering the probability and the severity of contingencies. Meanwhile, the economic value of DR is modelled and incorporated into the optimal operation solutions. Moreover, to enhance the computational efficiency, an iterative solution algorithm based on the Benders decomposition is developed to solve the formulated TEP problem. The proposed approach is numerically verified on the Garver's 6-bus, IEEE 24-bus RTS, and 2383-bus polish systems. Case study results demonstrate that the proposed approach can effectively investigate the impacts of large-scale integration of WP and DR on system operations and planning. Moreover, the proposed risk-averse approach is economically efficient and more robust to stochastic variations.

This study presents a novel equivalent, which is suitable for simulation of inertial and primary frequency control effects. In the model reduction procedure, dynamic power injectors are used to replace the external system and to mimic its dynamic behaviour. The parameters of the equivalents are tuned with a simple approach presented in this study. The effectiveness of the proposed method is demonstrated on a modified version of the European Network of Transmission System Operators for Electricity (ENTSO-E) dynamic study model. The results show that the system frequency response of the unreduced system is retained and a speedup of the simulations of around 4.0 is achieved.

This study presents a new approach for optimally designing a secondary distribution system (SDS) considering electric vehicle (EV) charging demand and in particular the high-power fast chargers in residential dwellings. The mathematical formulations of the optimal SDS design problem, which minimises the total annual cost per customer (TACCu) taking into consideration the voltage and the distribution transformer (DT) aging constraints, are developed. The proposed design approach, considering different house sizes and types, are also discussed. The obtained designs are then tested and verified through an IEEE 34-test distribution system and a real distribution system. The results have shown that the proposed optimal design approach was able to accommodate the EV charging demand without any violations of voltage and a DT aging constraints.

Accurate fault location methods are of major significance to the line maintenance of voltage source converter (VSC)-DC distribution systems. However, power electronics change the fault-characteristics, and the new fault theory is not fully mature. In addition, rapid fault isolation leaves little information for fault location. Fault location for VSC-DC distribution systems is still a big challenge. The proposed fault location method is developed based on an equivalent feeding-stage circuit derived herein. The line quantities sixth-harmonics are used, and an improved discrete Fourier transform algorithm (IDFT) is proposed to remove the influence of both decaying periodic and DC components. Simulation results on PSCAD/EMTDC verified the feasibility of the proposed fault location and IDFT algorithms. Using local measurements with the sampling frequency of 9.6 kHz, the method can locate fault position within 10 ms and with less than 2% error.

Energy poverty limits the economy and social development throughout the world. Wind turbine reduces the energy costs and facilitates the development of renewable energy industry, which provides an effective solution to energy crisis and environment pollution and develops rapidly in recently years. In this paper, a radial basis function neural network (RBFNN) optimisation model predictive control (MPC) was proposed for large wind turbines. In accordance with the complexity and uncertainty of wind turbine operation, a linear model based on the blade element momentum theory was established and the influencing factors of the proposed model were evaluated. The MPC taking into full account three degrees of freedom control multivariate was enforced by RBFNN prediction model, which meets the requirements of specified operation region. Additionally, the RBFNN prediction model with the memory of complicated rules and changed trend was trained by a great deal of historical data. The RBFNN in combination with MPC solves global optimisation problems and improves the dynamic performance of system. Simulation results for three-bladed 5 MW onshore wind turbine verified the effectiveness of the proposed method and confirmed the fact that the fatigue loads were significantly reduced in the turbine tower.

In this study, a multi-objective stochastic optimal power flow (SOPF) problem with the presence of uncertain wind power generations is introduced. In particular, this study has two main contributions. First, it proposes a multi-objective SOPF which consists of the operating cost, voltage stability and emission effects as the objective functions. The wind uncertainty is formulated as a scenario-based technique. Demand response program is considered in this study, which is one of the most efficient control ways to reduce the risk of voltage instability after a contingency occurrence or a stressed loading condition. In addition, the proposed approach uses the technique of fuzzification to normalise all objective functions and to find the optimal solution. The second contribution proposes a line voltage stability index (LVSI). The proposed LVSI can detect precisely the voltage collapse in comparison with other LVSIs, especially after the occurrence of a given contingency due to the dynamic elements of the system. The proposed multi-objective SOPF is also carried out with different existing LVSIs as the objective functions. These approaches are tested and validated by the modified WECC test system, the IEEE 39-bus.

An innovative method based on optimal injection current is introduced to calculate reactive compensation capacity during unbalanced three-phase distribution transformer operation. This method offers a new process that governs the three-phase unbalanced distribution transformers compared with susceptance compensation and calculates the operation capacity of the reactive power compensation electric control equipment through reverse thinking. Compensation effect is further optimised by adding reasonable restrictive conditions and neglecting inductive compensation combined with practical electrical engineering computing. This study presents the calculation method of optimal injection current with pure capacitive reactive power control along with its basic principles, algorithm implementation, and practical analysis. Results show that calculation based on optimal injection current avoids the overcompensation and overcurrent of susceptance compensation while improving the governance of the unbalanced three-phase distribution transformer effect.

Increasing electrical energy consumption and associated power grid expansion tend to increase the level of the fault currents in power systems. Maintaining proper functioning of the grid requires fault currents to be at an acceptable level using appropriate fault current limiting techniques. Most of the fault current limiter devices work in a saturated core region during normal operation mode. Since the continuous saturation core also requires continuous energy supply, the use of the cores that operate at low initial permeability region is a suitable candidate with no power consumption. For this purpose, the potential use of the SAE 1020 low carbon steel in the fault current limiter is investigated. The design simulation is carried out in terms of required shape, dimensions and parameters by using finite element analysis. The dimension of the fault current limiter is optimised by introducing air gaps into the core, while keeping the performance of the fault current limiter device unchanged. The simulation results indicate that the proposed design with air gaps still has a better current limiting performance compared to the coil with pure air core. Reducing the dimensions of the device is achievable in the expense of its performance.

This study employs Gaussian copula to model correlated non-normal variables in power system, whereby the probabilistic power flow (PPF) problem is transformed to independent standard normal space. In conjunction with univariate dimension reduction model, two quadrature rules: Gauss-logistic (GL) quadrature and Clenshaw–Curtis (CC) quadrature, are developed to calculate the moments of PPF solutions; for CC quadrature, the weights and nodes are given explicitly. Testing on a modified 118-bus system, it is found that CC quadrature converges more uniformly than the generally used Gauss–Hermite quadrature, and GL quadrature is more accurate.

This study presents a new hybrid algorithm to estimate the harmonic parameters of a distorted signal in power systems. The parameters to be estimated are amplitudes and phases of harmonic components according to the voltage/currents samples. The proposed algorithm is based on combination of the recursive least squares (RLS) and iterated extended Kalman filter (IEKF) techniques. The RLS–IEKF algorithm decomposes the problem into linear amplitude estimation and non-linear phase estimation leading to extracting the intended state vector in online mode and intensive noise presence. As well, RLS–IEKF estimates dynamic parameters using tuning factor which controls the impact of measurement on estimation process. Simulation results obtained by MATLAB show the accuracy and speed of convergence in comparison with that of conventional discrete Fourier transform and ensemble Kalman filter. For further validation, the proposed algorithm is implemented by C++ code and is applied to real switching current data. The real-time implementation of RLS–IEKF in a simple laboratory setup using PC/104 computer set and dedicated hardware shows its satisfactory performance for practical power quality and protection cases.

The authors consider the problem of estimating zero sequence parameters of a transmission line using synchrophasor data. When a set of three-phase transmission lines share, partial or complete, right of way, then their zero sequence models exhibit mutual coupling. As such the zero sequence parameters cannot be estimated by linear least squares or total least squares (TLS) technique which are the preferred methods when dealing with the positive sequence line parameter estimation problem. Further, method design has to factor the constraint of a sparse data set when dealing with zero sequence phasors. Therefore, the authors propose orthogonal distance regression approach for solving the zero sequence parameter estimation problem. This generalises the method of TLS to the non-linear parameter estimation problem considering noise in both the voltage and current synchrophasor measurements. Extensive case studies and comparative evaluations are presented to demonstrate the efficacy of the proposed approach.

The simultaneity of power systems development and uncertainty of system elements has promoted the importance of probabilistic load flow (PLF) in the operating and planning studies of the system. This clarifies that the use of the fast and accurate approaches for PLF computation is necessary. To achieve this objective, this study presents an analytical technique, based on the properties of Laplace transform (LT). The suggested methodology is applicable for every continuous probability distribution function as the input random variable. The proposed procedure is applied to the MATPOWER 9- and 118-bus test systems. To validate the combined cumulants and LT (CCLT) technique, the results are compared with the Monte Carlo simulation and the cumulants method combined with the maximum entropy (CCME) principle. The test results show that the proposed approach gives accurate results, with the lower computational burden comparing CCME. Furthermore, the method formulation and case study results demonstrate that the CCLT method is mathematically straightforward and computationally efficient.

This study presents a new control scheme for synchronisation of a photovoltaic (PV) system to a three-phase grid employing self-tuning concept. The proposed synchronisation approach is developed to mitigate load current harmonics, to extract maximum PV power and inject it into the grid. Maximum PV power is extracted using an incremental conductance-based algorithm for estimating the reference DC-link voltage of a voltage source inverter and a proportional–integral controller for regulating the actual DC-link voltage. In the proposed self-tuning filter (STF) algorithm, only a single module of STF is required to extract the fundamental components of load current and grid voltage thus reducing system size, complexity and overall response time. Simulation of the proposed system is carried out using MATLAB/Simulink followed by experimentation on a prototype of grid connected PV system developed in the authors' laboratory. The efficacy of the proposed STF control scheme is compared with a recently reported algorithm called improved linear sinusoidal tracer (ILST) and dual second-order generalised integrator (DSOGI). From the comparison with DSOGI and ILST, it is observed that the STF-based control scheme provides faster response and reduces total harmonic distortion of the grid current despite the distorted and unbalanced grid condition.

In this study, the probabilistic collocation method (PCM) is proposed to construct a stochastic correlation model of wind speeds at neighbouring wind farms and solve probabilistic power flow (PPF) of South Australia (SA) grid. Based on the historical sampled wind source data, the model is developed to reduce the number of uncertain parameters of the power system model by considering the spatial correlation of wind speeds between neighbouring wind farms. Furthermore, this model aims to increase the computational efficiency of PCM when dealing with PPF simulation. Finally, the computation efficiency and accuracy of the PCM, compared with traditional Monte Carlo simulation method, are validated by the simulation results of aggregated power flow model of SA case studies.

This work aims to carry out a comprehensive analytical evaluation of a quasi-Z-source matrix converter (qZSMC), which is comprised of a three-phase quasi-Z-source network (TPQZSN) followed by an MC, as the grid interface of a permanent magnet synchonous generator (PMSG)-based wind energy conversion system (PMSG-WECS). First, the small-signal model of the TPQZSN in space domain is obtained by the state-space averaging method, while the parasitic elements are also considered. The TPQZSN's transfer functions are further calculated and analysed. The results together with a frequency-domain analysis are used to render a comprehensive guide for the selection of proper passive components of the TPQZSN. The qZSMC is then employed as the grid interface of a PMSG-WECS. A control system is designed for the grid-connected operation. The small-signal model of the whole system is developed. The qZSMC is compared with conventional MC (CMC) from stability point of view, indicating that, unlike the CMC, the qZSMC has no trouble working stable for all ranges of output power. Furthermore, it is shown that the proposed WECS can operate stably without using digital filter. The system performance is compared with CMC-based WECS.

Conventional power system was originally designed to provide efficient and reliable power. With the integration of information technology and advanced metering infrastructure, the power grid has become smart. The smart meters have allowed the system operators to continuously monitor the power system in real time and take necessary action to avoid system failures. Malicious actor, with access to the smart meters can modify sensor measurements to disrupt the operation of power system. To make the power system resilient to such cyber-attacks, it is important to study all possible outcomes of cyber-intrusions. In this paper, we present an attack on security constrained optimum power flow. We show with the help of case studies how an attacker, by injecting false data in load measurement sensors, can force system operator to change the dispatch and hence make the power system N–1 in-compliant. The attack is modeled as a bi-level optimization problem, aiming to find the minimum set of sensors required to launch the attack. From the system operator's perspective, critical lines and critical generators vulnerable to false data injection (FDI) attack are identified. IEEE 14 bus and 30 bus test systems are used to test the vulnerability of the power system against FDI attacks.

In this study, a three-phase power-flow method based on graph theory, injection current, and matrix decomposition techniques is proposed for unbalanced radial distribution networks. A decomposed quasi-Newton–Raphson-based method is used to solve the set of non-linear power equations described in polar coordinates. By using the injection current technique, the coupling-free component models can be integrated into the proposed method. To validate the performance and effectiveness of the proposed method, four three-phase IEEE test systems and a practical Taiwan Power Company (Taipower) distribution system are used for comparison. The test results show that the proposed method exhibits robust convergence characteristics and high performance even for ill-conditioned distribution networks.

In this article, a maiden attempt has been made to derive an optimal and effective outcome of load frequency control problem (LFC) using a novel evolutionary algorithm called multi-verse optimisation. The main inspiration of this algorithm is based on three concepts in cosmology: white hole, black hole, and wormhole. To show the effectiveness, a four-area hydrothermal power plant with distinct proportional-integral-derivative (PID) controller is investigated at the first instant and then the study is forwarded to the five-area thermal power plant. To enhance the dynamic stability, an optimal PID plus double-derivative controller (PID + DD) is designed and included in the control areas. The superiority of the proposed method has been established over some recently addressed control algorithms by transient analysis method. To add some degree of non-linearity, generation rate constraint and governor dead band are included in the model and their impacts on the system dynamics have been examined. Finally, a random load perturbation is given to the test systems to affirm the robustness of the designed controllers.

This study presents a novel networked microgrid (MG)-aided approach for service restoration in power distribution systems. This study considers both dispatchable and non-dispatchable distributed generators (DGs), and energy storage systems. The uncertainty of the customer load demands and DG outputs are modelled in a scenario-based form. A stochastic mixed-integer linear program is formulated with the objective to maximise the served load, while satisfying the operation constraints of the distribution system and MGs. The interaction among MGs is modelled using the type 1 special ordered set. Two approaches are developed and compared: (i) a centralised approach where all MGs are controlled by a distribution system operator, and (ii) a decentralised approach where the distribution system and MGs are managed by different entities. The proposed restoration models are tested on a modified IEEE 123-bus distribution system. The results demonstrate the advantages of leveraging networked MGs to facilitate service restoration.

Due to the increasing integration of photovoltaic-based distributed generators (PV-DGs), uncertainties resulted from both PV-DG power and loads have posed a serious challenge in microgrid day-ahead scheduling and operation. In this study, the effect of uncertainties in both PV-DG power and loads on the microgrid day-ahead scheduling is assessed. Specifically, the correlation between the PV-DG power and load uncertainties is taken into account as this is closer to the reality. The probabilistic optimal power flow (P-OPF) model is formulated to analyse the impact of the correlated PV-DG power and load uncertainties. A modified Harr's two-point estimation method (MH-2PEM) is introduced to provide computation-efficient estimation of the P-OPF solution. Results obtained by using the MH-2PEM and Monte Carlo simulation are compared in an equivalent 44 kV distribution feeder system and the accuracy and efficiency of the MH-2PEM are verified. The variation ranges of the microgrid day-ahead scheduling solution resulted from uncertainties in PV power and load are obtained with various confidence levels.

Intermittent distributed generations, stochastic loads and other uncertainties have uncertain impacts on the node voltages in distribution network. To quantitatively assess the impact boundaries of uncertainties on node voltages, an interval model is proposed in this study from the perspective of linear description. The model parameters defined as the voltage influence intervals are solved for as follows: first, conduct the analysis sample collection through the affine power flow and the expected operating point estimated by point-estimate method; then, calculate the voltage influence intervals via interval regression for the above sample. To meet the different demand of solving efficiency and conservative level for parameter identification, two solution methods of interval regression based on quadratic programming and stochastic programming are proposed, respectively. To further illustrate the practical application of the interval model, an optimal location method for centralized energy storage is presented. The verification results based on the modified IEEE 33-bus system and practical 113-bus system demonstrate the interval model and its solution methods have an excellent performance in assessing the impact boundaries of uncertainties. The application case also shows that the proposed decision method can produce a global and robust location scheme with considering the bound uncertainty of distributed generation output fluctuations.

Dissolved gas analysis (DGA) is a popular method for diagnosing faults inside power transformers. However, some of the recorded data for the analysis are with ambiguous characteristic, leading to misdiagnosis of conventional methods. In this work, a hybrid method, which combines the relevance vector machine (RVM) and the adaptive neural fuzzy inference system (ANFIS) has been proposed to address this issue. Given the fuzziness between DGA records and fault type, and to minimise the number of rules that ANFIS needs to extract, the RVM algorithm performs binary separation firstly, and then ANFIS is utilised to achieve further fault diagnosis in this study. The experimental results demonstrate that the hybrid RVM–ANFIS algorithm can achieve an accuracy rate as high as 95%. Moreover, the proposed algorithm exceeds single ANFIS, support vector machine, and artificial neural network on distinguishing multiple faults and samples with ambiguous characteristic. The engineering application results also demonstrate the effectiveness and superiority of the proposed RVM–ANFIS.

In modern power systems, increasing faults of overhead transmission lines are caused by wind swing discharge especially in weather-sensitive regions such as coastal hurricane-prone areas. Since the vast majority of wind swing discharge accidents occur at the working voltage and existing models established for the suspension insulator wind swing discharge are not adequate to analyse all cases, an improved model under wind with arbitrary directions is proposed in this paper. Comprehensive factors are taken into consideration including exposure factor of wind pressure under different landforms and different elevations, wind pressure asymmetric factor, iced conductors, the coupling effect of conductors to insulators, the shielding wake flow effect of bundled conductor, the fluctuating wind amplification effect, mechanical oscillating of insulators, etc. IEEE model and design manual model are chosen as comparisons with the simulation results of ANSYS. Test results show that the improved model proposed in this paper coincides with ANSYS results better than the other two models almost in all the working conditions considering different spans and altitude differences. The results demonstrate that the improved model can make contribution to the economical and reasonable design of towers as well as early warning of wind swing discharge of suspension insulators.