Large penetration of electrical energy storage (EES) units and renewable energy resources in distribution systems can help to improve network profiles (e.g. bus voltage and branch current profiles), and to reduce operational cost as well as power losses. On the other hand, unsecure system operation as a result of involving these units is another challenge to network operators. Therefore, establishing a trade-off between operational cost and security is very important. This study presents a new approach to determine the optimal charging/discharging schedule of EES units in distribution systems by employing multi-objective optimisation methods, which will effectively reduce operational cost and enhance distribution network security. In this regard, a voltage stability index (VSI) is converted into a security index to improve the radial network security. This VSI index is treated as a separate objective function, and a multi-objective strategy is implemented to obtain a set of non-dominated solutions instead of a single optimal solution, which simultaneously minimise both of the operational cost and security index. In order to assess the effectiveness and applicability of the proposed method, it is applied to IEEE standard 33-bus and 136-bus distribution test systems, and then the obtained results are compared with those of existing methodologies.

Intentional islanding has been extensively studied recently as the last resort to prevent blackouts, mostly from the perspective of thermal static constraints satisfaction. However, as most previous studies on this topic do not address stability issues, their controlled islanding plans might fail to ensure the stability of resulted islands, thereby delaying their acceptance and adoption by utilities. This study makes progress towards addressing stability issues, proposing a controlled islanding model that ensures and improves the transient stability of the islanded system. Linear transient stability constraints are derived off-line, based on the extended equal area criterion, to ensure the first swing transient stability of the synchronous machines, just after the controlled line switching. The islanding model with transient stability constraints is first developed as a mixed-integer nonlinear program (MINLP). Furthermore, the MINLP model is linearised, resulting in a computationally lighter mixed-integer linear program. The objective function of the islanding model is to minimise the generation imbalance of islands and to increase the transient stability margin of the resulting islands, and the obtained optimisation results are validated by the fully fledged dynamic simulation. The efficacy of the proposed method is validated by simulation on the IEEE 118-bus system.

In this paper, real-time parameter estimation based intelligent controller has been proposed for the optimum automatic generation control (AGC) operation of multi-area interconnected power system. In earlier AGC studies, supplementary controllers were designed for presumed power system dynamic conditions, such as constant system loadings, fixed values of power system dynamic model parameters. However in practical AGC system, these power system model parameters, namely frequency bias parameter B, steam chest and reheater time constants & , power system time and gain constants & , load sensitivity factor D, etc., continuously varies depending upon the consumer's load demand and number of power generating units participating in AGC. The controller's operation no longer remains optimum as these system parameters changes from their initial values. In view of the above, parameter estimation based real-time intelligent controller has been designed which automatically adjusts its controller gain settings to its optimum conditions in real-time after sensing changes in power system model parameters. At first, different methodologies are presented to estimate dominant power system model parameters. Then, presented methodologies are utilised in the design of intelligent controller for AGC system. The proposed controller is successfully tested upon IEEE 39 bus system with various case studies.

A regional power grid containing high-density wind power is generally connected to a load centre through long-distance transmission lines. Voltage problems, caused by wind speed fluctuation, in the sending-end grid have become increasingly serious. The variable speed wind turbine has been used to compensate the deficit in reactive power. However, the available methods could not meet the requirement because the controllable range of reactive power contributed by wind farms cannot be adjusted. A new idea of reactive power control realised by the active control before wind speed fluctuation was proposed to prevent the voltage variation. Firstly, the influence of wind speed fluctuation on voltage was analysed, and the power controllable range of wind turbines was studied. The principle and strategy of the active control of reactive power were proposed based on the model predictive control theory. Then, the active control model was established according to the system dynamic demand for reactive power. According to wind speed forecasting, the reactive control capability of wind turbine was excavated to meet the grid demand by adjusting active power before wind speed variation, and the reduced active power of wind farms is optimally compensated by thermal powers. Finally, the method was proven to solve voltage problems.

To further improve fault diagnosis accuracy, a new hybrid feature selection approach combined with a genetic algorithm (GA) and support vector machine (SVM) is presented in this study. Adaptive synthetic technique and arctangent transformation method are adopted to improve the statistical property of the training set (IEC TC10 dataset). Five filter methods based on different evaluation metrics are employed to rank 48 input features derived from dissolved gas analysis (DGA). Then, feature combination methods are applied to aggregate feature ranks and form a lower-dimension candidate feature subset. The GA–SVM model is implemented to optimise parameters and select optimal feature subsets. 5-fold cross-validation accuracy of the GA-SVM is used to evaluate fault diagnosis capability of feature subsets and finally, a novel subset is determined as the optimal feature subset. Accuracy comparison manifests the superiority of the optimal feature subsets over that of conventional approaches. Besides, generalisation and robustness of the optimal subset are validated by testing DGA samples from the local power utility. Results indicate that the optimal feature subset obtained by the proposed method can significantly improve the accuracies of power transformer fault diagnosis.

This study presents a novel mathematical formulation to describe repairable components reliability model based on their bathtub curve and repair rate behaviour. The model is derived from the concept of Markov chain, which allows defining component's lifetime process. In addition, the formulation brings components’ degradation quantification. The proposed approach presents a pathway to develop an accurate reliability model for reliability assessments as shown in the presented case study.

A method to estimate soil parameters of horizontally multilayered earth model in frequency domain is developed with considering high-frequency field. The theoretical formula of complex apparent resistivity with considering high-frequency field is derived from Green's function. To avoid time-consuming numerical integration, complex image method is introduced to solve Sommerfeld integral. Simulated annealing algorithm is applied to optimise soil parameters. The accuracy of this method is confirmed by interpreting field data under the DC field. As the inversion results of this method are different from other literature's results in a four-layer model, the posterior analysis is made. Then this method is applied to the inversion of frequency domain soil parameters under both quasi-static field and high-frequency field. The differences of complex apparent resistivity distribution between quasi-static field and high-frequency field are compared at different frequencies.

In recent years, continuously increasing load demand and deficiency of capital resources vis-à-vis a competitive electricity market have forced transmission and distribution utilities worldwide to maximise the efficiency and utilisation of their existing infrastructure. This study presents a multi-objective approach to maximise the loadability of distribution networks by simultaneous reconfiguration and optimal allocation of distributed energy resources using a comprehensive teaching-learning-based optimisation algorithm. The proposed technique is based on the ɛ-constraints method and uses a graphical approach for network reconfiguration. Loadability enhancement is validated on the IEEE 33-bus and 69-bus radial distribution systems. Several case studies are carried out to demonstrate the effectiveness of the proposed approach in reducing the network active power losses, improving the kVA loading margins and enhancing the voltage profiles while considering voltage and thermal limit constraints. Results show that maximum loadability is obtained with simultaneous network reconfiguration and multiple DER allocation with varying power factors.

A novel generalised Hopfield neural network (GHNN) based self-adaptive proportional–integral–derivative (PID) controller for load frequency control (LFC) is designed for a two-area interconnected power system with nonlinearities of generator rate constraint and governor dead band. The control problem is conceptualised as an optimisation problem with an objective function as an area control error in terms of the PID controller parameters. The differential equations governing the behaviour of the GHNN were solved to obtain the controller parameters K _p, K _i and K _d. To test the feasibility and robustness of the proposed controller, the system is tested in the presence of randomness in load demands, imprecisely modelled system dynamics, nonlinearities in the system model and uncertainties in the system parameter variations. The proposed method is simulated using Matlab R2014b/Simulink and the results obtained have shown that the propounded controller performance is superior to the integral, PID and fuzzy-based proportional–integral controllers. In addition, the Lyapunov stability analysis of the overall closed-loop system was carried out and the controller is implemented in real-time digital simulator run in hardware-in-the-loop to validate the effectiveness of the proposed method. Furthermore, the proposed controller is applied to the three-area power system to test its adaptability.

Load flow (LF) is an extensively used tool in planning and operation of power systems. Formulation of LF problem can be assimilated as a set of autonomous ordinary differential equations, therefore, many numeric methods can be used to solve this problem. However, LF methods often need to compute one or more Jacobian matrix inversions in each iteration. Owing to this fact, these methods might not be computationally efficient. In this study, the authors propose combined Runge–Kutta Broyden's LF (RK4B) method in order to reduce the required Jacobian matrix inversion to only one in the first iteration. In this proposed method, Broyden's approach is employed in fourth-order Runge–Kutta method. In addition, two modifications of the proposed method are presented to reduce the number of iterations and improve the computational performance. The proposed method and the two modifications are validated using several well- and ill-conditioned cases. Results show that the combined approach has better computational performance than the classical multistage numeric methods, besides it preserves the robustness features of fourth-order Runge–Kutta method.

This study proposes a novel prediction model for uni- and multi-variable forecast, where error feedback is added to the original forecast value predicted using the persistence model. The error feedback mechanism is constructed to find out the relationship between the errors and the original forecast values. Simulation studies are carried out using wind power data obtained from two databases, and the results demonstrate that the proposed model provides a more accurate and stable forecast compared to other methods. Based on this, the economic benefit of accurate wind power forecast has been analysed for power system dispatch, which aims to minimise the operation cost. The dispatch results of two scenarios have shown that accurate forecast result decreases the cost of reserve capacity, balancer set invoking capacity and the possibility of wind curtailment, which leads to more economic dispatch of power systems.

This study presents a novel water cycle algorithm (WCA)-based optimal control strategy with the purpose of obtaining an efficient operation of an autonomous microgrid. The proposed control strategy is based on the proportional–integral (PI) controllers, which are optimally designed by the WCA. The optimisation process depends on the simulation-based optimisation approach and the criteria of integral squared error are chosen as an objective function. The control scheme is applied to an autonomous, decentralised, operation of a microgrid with multiple electronically interfaced distributed generation units and their local loads. In the islanded mode, the proposed controller is used to control the voltages of the islanded system despite the microgrid load and topological variability and uncertainties. The frequency of the islanded system is dictated through the use of an internal oscillator. The effectiveness of the proposed controller is compared with that obtained using the genetic algorithm-based PI controller. The validity of the proposed control strategy is extensively checked based on simulation studies in the PSCAD/EMTDC environment under different operating conditions of the microgrid. With the application of the WCA-based optimal PI control scheme, the microgrid operation can be further enhanced.

Virtual power plant (VPP) is an entity that aggregates generation of distributed generation units. Thus, it is important to design a competitive framework which models the participation of the VPPs in the electricity market along with their trading with distribution company (DisCo). This study proposes a bilevel programming framework using the concept of multi-leader–follower game theory to determine the optimal contract prices of VPPs which compete against each other in the distribution network. The optimal prices are used for setting annual bilateral contracts with VPPs. The leader layer of the proposed bilevel problem includes VPPs, which try to maximise their profits, while the follower problem corresponds to the cost function of the DisCo, which aims to minimise the payments of supplying the forecasted demand. The DisCo optimisation problem is transferred by its Karush–Kuhn–Tucker optimality conditions, turning each VPP problem into an equivalent single-level optimisation problem. Some case studies are defined and implemented to assess the performance of the proposed scheduling model.

Electricity and natural gas networks are critical infrastructure for society, but the robustness of coupled networks has not been evaluated, even though both systems have strong interactions. This article proposes a novel graph theory-based methodology to assess the structural robustness of the coupled natural gas and electricity transmission networks in Spain while considering their interdependencies. Cascading failures were simulated in 22 case studies with different topologies, and the performance against random failures was evaluated. The results show that the investment programme proposed by both network operators ultimately improves the robustness of the interdependent electricity and natural gas infrastructure in Spain compared to the current system.

Online transient stability assessment (TSA) is of great necessity for fast awareness of transient instability caused by fault contingencies. In this paper, a non-parametric statistics based scheme is proposed for response-based online TSA. A critical clearing time-based stability margin index is defined as the predictive output and 14 kinds of severity indicators are proposed as input features for the TSA predictor. With no prior knowledge of the correlation structure, the non-parametric additive model is used as the basis of the predictor. To screen out the weakly correlated indicators and reduce the dimensionality of the input space, two-stage feature selection is fulfilled by non-parametric independence screening and group Lasso penalised regression successively. The predictor is then learnt by least-squares regression in the reduced multi-feature space. With phasor measurement unit measurements at generator buses, severity indicators can be computed in the real-time and fast evaluation of post-fault stability margin can be made by the offline-trained predictor. The effectiveness of the proposed non-parametric statistics based scheme is demonstrated in a modified New England 39-bus system and a practical 756-bus transmission system in China.

This study presents an advanced control structure aimed at the optimal economic energy management of a renewable energy-based microgrid. This control scheme is applied to energy optimisation in a microgrid with non-dispatchable renewable sources, such as photovoltaic and wind power generation, as well as dispatchable sources, as distributed generators, hybrid storage systems compound by battery bank, supercapacitors, hydrogen storage unit, and one electric vehicle charging station. The proposed controller consists of a Lyapunov-based hybrid model predictive control based on mixed logical dynamical (MLD) framework. The main contribution of the proposed technique is the assurance of the closed-loop stability and recursive feasibility, by a novel approach focused on MLD models, using ellipsoidal terminal constraints and the Lyapunov decreasing condition. Finally, simulation tests under different operational conditions are performed and the attained results have shown the safe and reliable operation of the proposed control algorithm compared to existing and well-known energy management techniques.

This study shows that traditional current-based phase-selection (TCPS) schemes are not applicable to wind power systems, and subsequently proposes two improved phase-selection schemes. On the basis of the practical TCPS criteria, derivations of the essential conditions for correct phase selection, which show that the positive-to-negative sequence current branch coefficient (PNSCBC) ratio at the relay location should be within a certain range, are presented. Wind power generation systems are weak-infeed systems; consequently, the PNSCBC ratio on the wind power side is approximately equal to the negative-to-positive sequence impedance (NPSI) ratio of the wind power system. However, the authors’ theoretical and simulation analysis results show that the NPSI ratio of a wind power system may exceed the range required for reliable phase selection; therefore, TCPS schemes are not applicable to wind power systems. Thus, they propose improved phase-selection schemes based on sequence current fault components and phase current difference fault components. In the proposed schemes, the PNSCBC ratio is obtained using the backside system impedance measured by the relay, and it is then used for compensating the TCPS criteria. Simulation results and field fault test data verify the efficacy of the proposed schemes.

One-terminal algorithms utilise only measurements at one end of the transmission lines for fault location, and thus, the required number of phasor measurement units (PMUs) is less compared to two-end algorithms. There have not been major studies in terms of reducing required number of PMUs to implement fault location algorithms based on single terminal data. This study develops a fault observability rule using the basic theories of power system observability. Then, this rule is used in some optimisation problems for determining the minimum number and optimal placement of PMUs to attempt the complete system observability in normal condition and complete or relative fault observability simultaneously. Moreover, here, a novel fault location algorithm is proposed, which uses one-terminal voltage and current data. To enhance the accuracy of fault location, the novel algorithm utilises positive bus impedance matrix of the network along with the voltage and current equations of the faulted line. The performance of the proposed fault location algorithm and optimal placement method of PMUs for power system and fault observability is investigated on 39-bus test system.

A huge motivation has recently made on microgrid (MG) financial issues, aimed to investigate the contribution of MG operator (MGO) and private investor to reach an optimal operational strategy. Motivating the private investors to contribute in an energy production, is a considering benefit sharing factor by MGO to satisfy both of MGO and private investor. In this study, a reliability-constrained optimisation approach is presented to calculate the number and size of MG system components. To this aim, planning problem is solved in two cases; full available state and state with considering random outage of units. Furthermore, all uncertainties of generation units are considered in the problem formulation. Non-sequential Monte Carlo method is used to generate all scenarios. The proposed model simultaneously optimises two objectives, namely the benefits of MGO. The two-stage heuristic method is used to solve the objective function. In the first stage, by utilising genetic algorithm, the solution to form the Pareto optimal front is found. In the second stage, to select the trade-off solution among obtained Pareto solutions, the fuzzy satisfying method has been used. Simulations are carried out in two cases, with and without considering the share of a private investor of MGO's benefit, i.e. β.

The authors address the problem of solving DC-optimal power flow (OPF) considering transmission losses in a large electricity network. The loss in a line is considered in the power balance equation and is taken as proportional to the absolute value of the flow through the line. Many standard solvers fail to converge to an optimal solution of the DC-OPF for comparatively large bus systems, even with a quadratic cost of generation. The authors use a decomposition algorithm such as alternating directions method of multipliers (ADMM) to address this problem. However, the ADMM algorithm cannot be directly applied to this problem because of the sparsity of the coefficient matrices of the objective function and the presence of inequality constraints. Thus, the authors introduce two relaxations to the DC-OPF problem, namely the regularisation and the modified penalisation. The authors provide a novel ADMM algorithm for the regularised and the modified penalised problem which converges to an optimal solution even for large bus systems. The authors show that the ADMM algorithm converges near to the optimal solution of the DC-OPF problem if the regularisation and modified penalisation parameters are chosen carefully. Numerical simulations illustrate the effectiveness of the algorithm.

A fault location (FL) identification method for smart distribution network is presented and validated using a digital real-time simulator (DRTS). The method can accurately identify the FL in a distribution network in the presence of distributed generation (DG). This method is based on state estimation (SE) algorithm which uses real-time data from simulated phasor measurement units (PMUs), placed in the distribution network. SE needs the fault currents of the generators and voltage measurements of an optimal number of nodes to perform the FL algorithm. The method was validated using the IEEE 37 node test feeder with DGs. PMUs are placed on the real-time model of the system. The real-time model was implemented on a DRTS which streams phasor data over the Internet using C37.118 protocol. OpenPDC is used to collect real-time PMU data coming from the DRTS. Microsoft SQL is used as a database management server to store data coming from OpenPDC. In the last step of the FL process, data stored in OpenPDC is fed into a FL identification algorithm to locate the fault. Both balanced and unbalanced fault types are applied to different nodes and an accurate estimation of the FL (over 90% of the cases) is achieved.

This study presents a probabilistic design of a power system stabiliser (PSS) for doubly-fed induction generator (DFIG) converter and investigates its potential capability in mitigating the sub-synchronous control interaction (SSCI) at multi-operating points. The aim is to improve the probabilistic sub-synchronous stability of the system with wind farm penetration. In this study, participation factors are obtained to identify the SSCI strong-related state variables and major control loops, which are used for the preliminary siting of the DFIG-PSS. Probabilistic sensitivity indices are then employed for accurate positioning of the PSS, selecting the input control signal and optimising the PSS parameters. The effectiveness of the proposed approach is verified on a modified two-area power system. The results show that the designed DFIG-PSS is capable of improving probabilistic small-signal sub-synchronous stability of the system at multi-operating points and its performance is better than a DFIG-PSS designed with the general small-signal method.

Islanding is an unusual condition in a power system where the generating station continues to supply the local load after one or multiple transmission line outage. This study develops a new islanding detection technique using the artificial neural network (ANN) classifier, which is provided with synchronised phasor measurements from a nine-bus Western Electricity Coordinating Council power system. An excessive number of data frames are generated in the phasor data concentrator. Before sending these data to the classifier, multiplier-based method (MBM) and Andrews plot-based method (APBM) are applied for dimension reduction and feature extraction. Comparisons are prepared with other dimension reduction algorithms. The accuracy of the classifier has been increased by increasing the number of hidden layers, the best accuracy is observed at a certain level for APBM. Non-detection zone (NDZ) for APBM is also evaluated. It is observed that the classification accuracy, and the detection time change when the neural network is retrained. All the results are compared and analysed statistically. This method can perform faster compared to other existing algorithms with an excellent accuracy and smaller NDZ.

In this study, the results of measurement for DC flashover characteristics of silicon rubber insulators in the presence of surface charges and in terms of geometric characteristics have been examined and analysed. In the carried out experimental tests, the surface of different insulators was charged by an external corona source and both the metal end fittings were kept grounded while the needles have been connected to a certain applied negative and positive DC voltages and then surface electric charge measured. A series of flashover tests have been carried out on the charged insulators under positive DC voltages to investigate the effects of surface charges. The experimental results revealed that positive electric charges reduced flashover performance while negative charges increased the flashover voltage level. Also, the experimental modelling of DC flashover of charged insulators regarding the ratio of shed spacing to shed depth and specific leakage distance in terms of surface electric charges, revealed that flashover voltage gradient of the charged insulator is most affected by specific leakage.

Power systems are required to achieve an optimally economic operating point while maintaining security and stability in the presence of credible contingencies. Transient stability constrained optimal power flow (TSCOPF) is a tool to bridge steady-state optimal power flow (OPF) with transient processes under a predefined set of simulated contingencies to guarantee post-fault rotor angle stability in a simulation time window. A parallel solution of TSCOPF using exact optimality condition (OC) decomposition is proposed, where generator swing equations are utilised recursively by exploring the structure of OCs of TSCOPF from the end of simulation time window to its beginning to derive an exact explicit expression consisting of generator-dynamics-related variables in terms of the steady-state variables. The OCs of the TSCOPF model are then decomposed into the OC of OPF, along with a parallel evaluation of this expression for each contingency. Multi-core processing units are applied to accelerate the evaluation process. Case studies with up to 1047 buses over 16 contingences demonstrate an 8× improvement in the computation for realistically sized power systems using the proposed decomposition strategy.

Communication data in the communication network of the islanded micro-grid can be disturbed in many ways, such as data attack or packet loss. To improve the control effect on the voltages and frequencies obtained by droop control of distributed energy resources (DERs) facing communication data disturbance (CDD), a response hierarchical control strategy is proposed in this study. Based on the concept of the cyber physical system, the control structure is divided into two layers: the cyber layer and physical layer. In the cyber layer, firstly, the effect of the CDD on the micro-grid system is analysed and then, an event-triggered data compensation method combining the back-propagation neural network and extreme learning machine is proposed in this layer to solve the problem of the CDD. In the physical layer, firstly, the droop control is used as the primary control to control the voltages and frequencies of DERs and then, a novel virtual leader-following consensus control method considering time-delay is proposed in this layer. Also, it is used to complete the secondary control of the voltage and frequency obtained by primary control. In the end, the simulation results confirm the effectiveness of the proposed hierarchical control strategy under CDD.

In this study, using the concept of setting groups (SGs), an adaptive protection scheme is proposed to increase the reliability of the system. Connection and disconnection of switches and distributed generators result in various scenarios for network topology changes. A hybrid genetic algorithm (GA) and linear programming (LP) method is utilised to solve the problem, where the GA, in a near-optimal manner, classifies the scenarios of the network topology changes into a limited number of SGs and the LP algorithm optimally coordinates the overcurrent relays within the SGs. Simulations are performed on a radial distribution network and a meshed distribution network. Although by increasing the number of SGs the average operating time of the relays is decreased, the number of changes in the relay settings is increased. Therefore, the multi-objective optimisation algorithm is used to determine, the desired number of SGs. The results show the efficiency of the proposed adaptive protection scheme.

The objective of this contribution was to study the behaviour of new and regenerated insulating oil used in power transformers under the influence of an electric stress. To estimate the degradation rate of the dielectric fluids, one thousand (1000) successive breakdowns were generated according to the IEC 60156 standard. The parameters such as dissipation factor (Tan δ), resistivity, total acid number (TAN) and oil water content were measured and examined following IEC/ISO standards. Good correlations have been obtained between TAN/resistivity and Tan δ which might provide a ‘picture’ of the fluid condition. The dissolved oxidation products for the two dielectric fluids (after the application of electric breakdowns) was evaluated by Fourier-transform infrared spectroscopy. The results obtained indicate that the degradation of the parameters is significant and confirms the influence of an alternative electric field (AC) on the new and regenerated oils. It was also suspected that inhibitors and antioxidants were removed from the oil after regeneration. Their concentration should therefore be monitored and replenished when necessary.