This study develops a system-level dynamic phasor (DP) model of a hybrid AC/DC microgrid. A new definition of DP, based on the multiple frequency shift transformation, is proposed to extend the models of a variety of AC/DC and DC/DC converters. The comprehensive control system is considered in developing the DP model of microgrid. Furthermore, a computational framework for applying this DP model into system-level simulation and small signal analysis (SSA) is given. The DP-based simulation is tested under conditions of environmental disturbances and unbalanced supply voltages. The SSA is performed on the developed DP model to evaluate the voltage stability and harmonic modes of the microgrid. Besides, this DP model shows high potential when implemented in the real-time simulation with larger scale system.

With the more precise day-ahead scheduling strategy for large-scale wind power integration, a model of stochastic security-constrained unit commitment (SCUC) under AC constraints is built and its corresponding solution is given here. A unit commitment model under AC power flow is developed based on network security constraints considering the uncertainty of wind power. In order to analyse this model in time, an ordinal optimisation is presented. The proposed method is verified by the numerical test on a modified IEEE-118 test system. The results show that the risk of voltage over-limit in large-scale wind power integration can be reduced effectively. Meanwhile, the validity of day-ahead generation schedule is enhanced. Furthermore, the computational efficiency of the proposed algorithm is improved significantly compared with that of the traditional one.

Here, a new bi-directional hybrid modular non-isolated DC–DC converter is proposed where it consists of a boost converter (BC) fed from the high-voltage (HV) side. At the BC output stage, a certain number of half bridge submodules (HB-SMs) is connected across the BC switch. During the turn-on period of BC switch, the HB-SMs are connected sequentially to the low-voltage (LV) side, which results in charging/discharging their capacitors from/into the LV side. While, during the turn-off period, the LV side is bypassed and the HB-SMs capacitors are connected in series across the BC output stage, which results in discharging/charging them into/from the HV side. The power flow is controlled in both directions by controlling the duty cycle. The proposed configuration provides self-balancing operation thanks to the sequential connection of HB-SMs capacitors, and it also provides the ability to operate with high conversion ratios. Illustration and analysis of the proposed converter and its closed-loop controller are presented. A full design of the values and ratings of the involved components are presented. Simulation study for a 2.5 MW (25 kV/10 kV) DC–DC converter is presented. Finally, experimental results for a downscaled prototype are presented for validation.

This study presents a digital coordination strategy of load frequency control (LFC) and Over/Under Frequency Relay (OUFR) protection for an isolated microgrid (MG) considering high penetration of renewable energy sources (RESs). In such MGs, the reduction in system inertia due to the replacement of traditional generating units with a large amount of RESs causes undesirable influence to MG frequency stability, leading to weakening of the MG. Furthermore, sudden load change and short circuits caused large frequency fluctuations which threaten the system security. In order to handle these challenges, this study proposes a specific design of the digital OUFR, which will operate for both conditions of over and under frequency in coordination with digital PID controller based on mapping technique in discretization process to protect the MG against high-frequency variations. To prove the effectiveness of the proposed digital coordination strategy, a small MG was investigated for the simulation considering load change, varying the penetration level of RESs and the system inertia. The results reveal the robustness of the proposed coordination to maintain the MG frequency stability and security. In addition, the superiority of the digital OUFR has been approved in terms of accuracy and speed response during high disturbances.

Electric distribution utilities are required to continuously deliver reliable electric power to their customers. Regulatory utility commissions often practise reward and penalty schemes to regulate reliability performance of utility companies annually with respect to a desired performance targets. However, the conventional regulation procedures are commonly found based on the customer-based standard reliability indices, which are not able to discern the service characteristics behind the electric meters and, hence, fail to holistically characterise the actual impact of electricity interruption. This study proposes a new method to evaluate the load-based reliability indices in power distribution systems using advanced metering infrastructure data. Furthermore, the authors introduce a reward/penalty regulation scheme for utility regulators to provide a reliability oversight using the proposed load-based reliability metrics. The new load-based reliability metric and the reward/penalty scheme proposed bring about superior advantages as the distribution grids become further complex with a high penetration of distributed energy resources and enabled microgrid flexibilities. Numerical analyses on different settings with and without microgrid considerations reveal the applicability and effectiveness of the proposed approach in real-world scenarios.

Wind energy curtailment has increasingly concerned the electric power industry. To absorb the surplus wind energy, a decentralised programme is presented via stochastically staggered dual tariffs for the coordination of plug-in electric vehicle (PEV) chargers. The programme consists of two stages: day ahead and real time. In the day-ahead stage, two steps are performed. First, the dual-tariff schemes are identified at the transmission level based on a charging coordination integrated unit commitment (CCIUC) model. Second, they are adjusted at the feeder level via a security check and correction (SCC) algorithm to ensure bus voltages, feeder currents and losses within desired limits. The CCIUC model and SCC algorithm incorporate a novel aggregated charging load model to grasp the integrated knowledge of PEVs in reacting to dual-tariff signals in an ex ante manner, which avoids real-time iterations between PEVs and systems. In the real-time stage, the individual charging pattern is produced for cost minimisation at the charging device level by a novel local coordination model. Simulation results show that the proposed program enables: (i) enhanced efficiencies on absorbing surplus wind energy at the transmission level; (ii) well considerations on satisfying regulation requirements at the feeder level.

The most common approach to risk assessment for power systems is based on the principle. Nevertheless, the economic rationale suggests its relaxation in cases where the consequences are relatively minor and exacerbation when they are large. This study addresses this need by proposing an alternative operational risk assessment methodology that is based on both probabilities and costs of possible contingencies. The foundations are built on listing all possible contingencies that may be considered by the transmission system operator (TSO). As only a subset of these contingencies can be examined in reasonable time, the upper and lower risk boundaries are introduced to quantify the risk underestimation. The ratio of those limits is used as an accuracy indicator, which – according to the desired level – may help the TSO to identify the required number of contingencies that have to be analysed. Furthermore, several approaches to improve the reduction of the number of simulated contingencies are discussed and the results obtained basing on the dynamic IEEE39 model are presented.

Since the discovery of dissolved gas analysis (DGA), it is considered as a leading technique for the diagnosis of liquid insulated power equipment. However, accurate analysis results can only be achieved if the measured gases closely reflect the actual equipment condition to enable an appropriate interpretation of these gases. In general, conventional techniques such as the ratio method, key gases, and Duval triangle combined or not with artificial intelligence techniques such as machine-learning algorithms are used for DGA interpretation. Here, four well-known machine-learning algorithms are compared in terms of DGA fault classification – Bayes network, multilayer perceptron, k-nearest neighbour, and J48 decision tree. Moreover, the effect of applying ensemble methods such as boosting through the Adaboost algorithm and bootstrap aggregation (bagging) is analysed, and the performances of these algorithms are evaluated. The data for developing classification models was transformed into three forms, other than the raw data. The obtained results clearly presented the efficiency and stability of some algorithms such as the J48 tree and Bayes networks for DGA fault classification, in particular, when the data is appropriately pre-processed. Moreover, the performance of these algorithms was found to consistently improve by integrating the concepts of multiple models or ensemble methods.

Single-ended travelling-wave-based protection has been available for several years, providing the advantages of low cost and no requirement for communication and synchronisation with the remote end. However, conventional single-ended travelling-wave-based protection has low reliability due to its use of only partial fault information, and it has high dependence on extracting a second reflected wavefront. A new single-ended travelling-wave-based protection algorithm using the 2 ms full-waveform post-fault signal in the time–frequency domain, which avoids the discrimination of the reflected wavefront, is presented. First, the full-waveform representation of the travelling wave in 3D subspace is proposed. The fault characteristics of the wideband travelling wave are analysed at different scales. Second, for faults that occur within one line or on adjacent lines, the propagation characteristics and reflection and refraction process are analysed in detail. Then, the correlation and difference of the full waveform are presented qualitatively and quantitatively. Finally, a time–frequency spectrum matrix is established based on the full waveform, and the protection algorithm is developed by using a time–frequency spectrum matrix and wave matching technology. Extensive simulations under different conditions verify the wide applicability and high reliability of the proposed algorithm.

In view of the frequent failure of the ball-eye (BE) in the transmission line, the force analysis of BE in extreme weather is carried out, and a set of bending fatigue test equipment was designed. It is the first time to carry out the fatigue test of BE treated by normalising, annealing and quenching and tempering process under bending loads of 2330, 3495 and 4560 N. Meanwhile, the bending fatigue life curve is plotted. From the macro and micro point of view, the characteristics and the content of the elements of the fracture, and the influence of the internal structure on the mechanical properties of three samples of different heat treatment processes are analysed. Conclusion indicates that the mechanical properties of the BE treated by normalising process are best, whose microstructure is fine and uniform ‘ferrite + pearlite’. Comparing the life of BE withstood tension load, it is revealed that bending load is the main reason to accelerate the failure of BE.

This study presents a fast, secure and reliable algorithm for the detection and classification of power system disturbances including high-impedance faults (HIFs). The proposed algorithm utilises mathematical morphology (MM) techniques, where the non-linear MM characteristics are exploited by strategic cascading of appropriate filtering functions to form a multistage morphological fault detector (MFD) for the extraction of features necessary for the characterisation of HIFs. The target features of the HIF are the randomness and arc extinction and re-ignition/unsymmetrical characteristics. The reliability and robustness in the extraction of the desired HIF features are enhanced by a weighted convex structuring element designed based on the attributes of power system signals. The performance of the proposed algorithm is tested under different types of disturbances including cases of HIFs on different contact surfaces. Moreover, the effectiveness of the algorithm is tested under noise condition to demonstrate its performance of the proposed MFD. All tests are simulated using IEEE13 bus test system.

Discharge voltage prediction of practical air gaps in transmission projects is a long-sought goal and also a great challenge in high-voltage (HV) engineering. An approach combined electric field simulation, feature extraction and machine learning algorithm is presented in this study to predict the switching impulse discharge voltages of extra-HV (EHV) and ultra-HV (UHV) transmission lines–tower air gaps. Some features extracted from the electrostatic field distribution are used to characterise the air-gap configuration and taken as input parameters of a prediction model established by a support vector classifier (SVC). Three kinds of actual gap configurations in EHV and UHV transmission lines are taken as test samples to validate the validity of the SVC model. Trained by experimental data of rod–plane gaps and one of the engineering gap configurations, this model is able to predict the discharge voltages of the other two conductor-tower gaps with acceptable accuracy. The mean absolute percentage errors of the three prediction results are 6.84, 4.19 and 3.46%. This research demonstrates the feasibility of discharge voltage prediction for complicated engineering gaps, which is useful to reduce the costly full-scale tests and helpful to guide the external insulation design.

To avoid the failure of instantaneous overcurrent relays (50) owing to fails triggered by transient inrush currents during capacitor-bank switching, this study describes a new approach to detect and classify high-transient inrush current. These currents are due to the energisation of a shunt capacitor bank, rated 4 × 72 Mvar/230 kV, in a substation system in Thailand. The simulation tool power systems computer-aided design (PSCAD) is used to simulate the transient inrush current using six transient-mitigation methods, i.e. (i) a base case, (ii) a pre-insertion resistor, (iii) a pre-insertion inductor, (iv) a current-limiting reactor, (v) a series 6% reactor and (vi) synchronous closing control. Inrush current signals from PSCAD were used as inputs for discrete wavelet transforms. On a scale from 1 to 30, the maximum value of the wavelet coefficient is used to detect the inrush current. The high value of the standard deviation of the wavelet-scale analysis is used to discriminate between the high transient inrush current and the normal capacitor rate current. The results obtained show that the newly proposed method effectively detects and discriminates the capacitor switching inrush current, both isolated and back-to-back switching, with high accuracy.

This study presents an innovative design for the optimal sizing, placement, and dispatch approach of distribution static var compensators (D-SVCs) in a radial power distribution system to improve the technical and economic aspects of the grid. The approach incorporates the total harmonic distortion (THD) effects into the assessment with the presence of non-linear loads. A multi-state particle swarm optimisation algorithm is also proposed, at first to select the placement and size and then to select the dispatch strategy of D-SVCs. Three IEEE test systems were used for the case study to show the efficacy of the method. The results reveal that the approach is viable, and it determines the cases where the highest savings were achievable fulfilling the grid voltage and THD constraints.

The thermal-electric coupling characteristics of combined heat and power (CHP) units make it critical problem to improve wind power accommodation ability in the heating season. This study establishes a CHP dispatch model for better integration of wind power based on electric boiler with thermal storage (EBTS). A start–stop strategy of EBTS is formulated that takes only the abandoned wind as the heat source. The electric boiler runs at maximum power during the wind curtailment, and the heat output of EBTS is changed by controlling the endothermic and exothermic rates of the thermal storage. Considering the scheduling difficulty of the CHP system with EBTS, the multi-agent model of heat and electricity is built. Through information exchange and load distribution between the agents, electric load is balanced by all units while thermal load by CHP units and EBTS. Finally, the Newton–Raphson iterative method is applied to solve the proposed model. The results of numerical examples validate effectiveness and economic improvement of the proposed method.

Traditionally, electric system operators have dispatched generation to minimise total production costs ignoring the flexibility of the AC transmission system (ACTS). One available option to enhance power system security is to harness the flexibility of the ACTS, where a variety of flexible AC transmission system (FACTS) devices can be incorporated in the ACTS. However, utilisation of FACTS devices is limited today due to the complexities that these devices introduce to the AC optimal power flow (ACOPF) problem. The mathematical representation of the full ACOPF problem, with the added modelling of FACTS devices, is a non-linear programming (NLP) optimisation problem, which is computationally burdensome for large-scale systems. This study presents a method to convert this NLP problem into a mixed-integer linear program (MILP) where a certain level of solution accuracy can be achieved for a time budget. In this regard, this study first proposes a linear AC OPF model, using which the OPF solution with the operation of FACTS devices is obtained. In addition, the loadability of the power systems is utilised to quantify the impacts of FACTS devices on improving the security of system. The OPF problem including FACTS devices based on a linearised model is tested on a 6-bus and the IEEE 118-bus test systems.

The influence of a non-flat uneven soil surface on the soil apparent resistivity measurements in unidimensional Vertical Electrical Sounding is analysed. The sounding in a real uneven soil surface is simulated by using a soil model, which allows the semi-analytical expressions valid for perfectly plane soils to be used. In addition, real Wenner electrodes properly calibrated are used, and the differences with a sounding in an equivalent soil with a flat surface are highlighted. Due to the loss of symmetry around the centre of the Wenner array, measurements along different directions are needed and the mean value of the distributions of the measures is assigned to the value of the apparent resistivity. Furthermore, for the purpose of reducing the width of such distribution, the use of electrodes of increasing length is suggested within limits. Finally, the proposal of a multi-layered soil model with an uneven surface based on the Wenner measurements is discussed.

In this study, a customer reward scheme is proposed to build an effective demand response program for improving demand elasticity. First, an objective function has been formulated based on the market operation and an optimal incentive price has been derived from this objective function. Second, the incentive price is employed as a part of a reward scheme to encourage customers to reduce their electricity demand to a certain level during peak hours. Two typical customer response scenarios are studied to investigate the impact of customer response sensitivity on the loss of utilities’ and customers’ profits. Finally, a dataset for the state of New South Wales, Australia is employed as a case study to examine the effectiveness of the proposed scheme. The obtained results show that the proposed scheme can help improve the elasticity of demand significantly thereby reducing the associated financial risk greatly. Moreover, the proposed scheme allows customers to get involved voluntarily and maximise their profits with minimum sacrifice of their comfort levels.

A regional network of microgrids includes a cluster of microgrids located in a neighbourhood area connecting together through power lines. In this study, the problem of operation management of networked-microgrids is considered. The main goal is to develop an efficient strategy to control local operation of each microgrid including the amount of energy to be requested from the main grid and the optimal charging/discharging patterns of batteries along with the transferred power among microgrids considering system's technical constraints. Accounting for system uncertainty due to the presence of renewable energy sources and variability of loads, the problem is formulated in the framework of chance-constrained model predictive control. Moreover, the Monte Carlo algorithm is adopted to generate discrete random scenarios to evaluate the solutions. Simulation studies have been exemplarily carried out in order to show the effectiveness of the proposed approach.

This study recommends a stochastic optimization model for the security constrained unit commitment (SCUC), which incorporates the optimal transmission switching (OTS) for managing the uncertainty of wind power generation and equipment failures, i.e. unit/line outages. Also, this study presents a technique in stochastic SCUC model with the OTS action using the AC optimal power flow (AC OPF). The AC OPF provides a more accurate picture of power flow in the power system compared to the DC optimal power flow that is usually considered in the literature for the stochastic SCUC models and the OTS action. While the stochastic SCUC model with the OTS action based on AC OPF is a mixed-integer non-linear programming model, this study transforms it into a mixed-integer linear programming (MILP) model. The MILP approach uses a piecewise linear model of AC OPF, which allows the reactive power and voltage to be considered directly in power flow model. The proposed stochastic SCUC problem is evaluated on the 6 bus, IEEE 118-bus and 662-bus test systems in pre- and post-OTS action. Obtained results demonstrate the effectiveness of the proposed model.