This study investigates the application of support vector clustering (SVC) for the direct identification of coherent synchronous generators in large interconnected multi-machine power systems. The clustering is based on coherency measure, which indicates the degree of coherency between any pair of generators. The proposed SVC algorithm processes the coherency measure matrix that is formulated using the generator rotor measurements to cluster the coherent generators. The proposed approach is demonstrated on IEEE 10 generator 39-bus system and an equivalent 35 generators, 246-bus system of practical Indian southern grid. The effect of number of data samples and fault locations are also examined for determining the accuracy of the proposed approach. An extended comparison with other clustering techniques is also included, to show the effectiveness of the proposed approach in grouping the data into coherent groups of generators. This effectiveness of the coherent clusters obtained with the proposed approach is compared in terms of a set of clustering validity indicators and in terms of statistical assessment that is based on the coherency degree of a generator pair.

This study proposes a Pareto-based multi-objective distribution network reconfiguration (DNRC) method using discrete particle swarm optimisation algorithm. The objectives are minimisation of power loss, the number of switching operations and deviations of bus voltages from their rated values subjected to system constraints. Probabilistic heuristics and graph theory techniques are employed to improve the stochastic random search of the algorithm self-adaptively during the optimisation process. An external archive is used to store non-dominated solutions. The archive is updated iteratively based on the Pareto-dominance concept to guide the search towards the Pareto optimal set. The method is implemented on the IEEE 33-bus and IEEE 70-bus radial distribution systems, simulations are carried out and results are compared with other available approaches in the literature. To assess the performance of the proposed method, a quantitative performance assessment is done using several performance metrics. The obtained results demonstrate the effectiveness of the proposed method in solving multi-objective DNRC problems by obtaining a Pareto front with great diversity, high quality and proper distribution of non-dominated solutions in the objective space.

Energisation of large power transformers may cause significant voltage dips, of which the severity largely depends on a number of parameters, including circuit breaker closing time, transformer core residual flux and core saturation characteristic, and network conditions. Since most of the parameters are of stochastic nature, Monte Carlo simulation was conducted in this study to stochastically assess the voltage dips caused by transformer energisation in a 400 kV grid, using a network model developed and validated against field measurements. A dip frequency pattern was identified over 1000 stochastic runs and it was found to be sensitive to residual flux distribution but insensitive to closing offset time distribution. The probability of reaching the worst case dip magnitude (estimated under the commonly agreed worst energisation condition) was found to be lower than 0.5%; about 80% of the dips are likely to be with magnitudes lower than 0.6 pu of the worst case. Nevertheless, there are dips with magnitudes exceeding the worst case dip magnitude, indicating the inadequacy of deterministic assessment approach by using the commonly agreed worst energisation condition.

Estimation of total harmonic current generated by a group of sources is a difficult task, because of time variations of loads and background harmonic voltages caused by the rest of the network. Several simulation techniques have been proposed in the past, which try to address the problem and estimate the maximal levels of current and voltage distortion, but with limited field measurements to verify the results. In this study, the subject of the summation of harmonic currents in industrial installations is reviewed and analysed based on field measurements. Harmonic magnitudes and phase angles of individual load currents and the total current were measured simultaneously, on four locations. The duration of each measurement was approximately 1 week, with a time resolution of 1 s. The assumptions made in probabilistic studies are discussed based on the results, and coefficients for simplified estimation are calculated together with their limit values. Lastly, some considerations about the measurement approach are made.

This study proposes a new recurrent differential evolution (RDE) technique for optimising the multi-area, static/dynamic joint energy and reserve dispatch problem with complex equality and inequality constraints such as ramp-rate limits, tie-line constraints and reserve and capacity coupling constraints. Generator contingencies are also simulated and their effect on operating cost and reserve dispatch is observed. The proposed RDE has a two-phase mechanism which is particularly suitable for large dimensional problems having complex search spaces. During the first phase, massive exploration of the problem domain is conducted. The solutions obtained in the first phase are extensively exploited during the second phase. The proposed algorithm is tested on (i) A 2-area, 6- generating unit system (ii) A 5-area, 24-unit system and (iii) IEEE 57-bus system with 17 generating units. The proposed RDE is found to eliminate the dependence of classical DE on control parameters and produces better quality solutions for complex problems.

Distribution system planning is undergoing a change in paradigm in the context of deregulation and with penetration of distributed generation (DG) sources into distribution networks. This study presents a new heuristic approach based on a back-propagation algorithm combined with cost–benefit analysis to comprehensive multi-year distribution system planning. The planning problem incorporates various energy supply options for local distribution companies such as DG sources, substations and feeders and determines the optimum capacities, locations and upgrade plans. Test results comprising a 32 bus system and the IEEE 69 bus system are presented to illustrate the performance of the proposed method. The results are compared with an optimisation solution, and it is demonstrated that the heuristic approach can achieve better performance and has higher computational efficiency.

This study describes a model for optimal scheduling of hydro thermal systems with multiple hydro reservoirs. Inflow to hydropower reservoirs, wind power and exogenously given prices are treated as stochastic variables. Power flow constraints are included through a linearised power flow model. A linearised representation of start-up costs for generating units and pumps is provided. The model is well suited for medium- and long-term hydro thermal generation scheduling and has the capability of capturing detailed system constraints by using a fine time resolution. The presented model is tested on a realistic representation of the Icelandic power system, considering some potential future system extensions.

In this study, a fast and fully software-based algorithm for digital phase-locked loop (PLL) is proposed via a new hybrid approach in software and hardware by using an advanced digital signal processor architecture. The proposed algorithm is robust against line disturbances such as phase-angle jump, voltage sag, third harmonic injection, multi-zero crossing and step change in frequency at the input voltage. Performance and robustness of the proposed method are investigated through experimental studies. Furthermore, it is compared with three different PLL algorithms in detail to show its superiority over existing methods.

By increasing the spinning reserve capacity, reliability of the power system is improved, however, total cost is increased. Therefore to supply the spinning reserve of power system, a compromise between reliability and cost should be considered. In this study, by considering the value of lost load for each important customer, the spinning reserve capacity is determined based on minimisation of total cost of problem. In addition, to supply the spinning reserve of power system, a new market player called plug-in electric vehicles (PEVs) aggregator is proposed. In this base, the aggregator's self-scheduling problem for participating in spinning reserve market is modelled using an agent-based model and behaviour of the PEVs fleet is modelled from the aggregator's point of view. To improve the accuracy of results, the market players are considered as price makers and their offers to the market are modelled using a dynamic game theory simulation. The numerical results show that the presence of PEVs aggregator in spinning reserve market has desired impacts on power system reliability and total cost of system.

This study proposes a multi-objective fuzzy-based procedure for solving reactive power management in competitive environment. The proposed procedure incorporates both economical and technical aspects of reactive power support. The economical aspect aims to minimise the total costs of reactive power purchase from service providers as primary objective function, whereas the technical aspect is taken care of through the secondary objective function that minimises the total transmission losses. The proposed procedure achieves security constraints such as the bus voltage limits, reactive power capability limits and transmission line reactive transfer limits. The proposed procedure is applied to the west-delta region system as a part of the Egyptian Unified network. The numerical results show that the proposed procedure achieves a minimum real power loss with maximal reactive reserve for power systems for different operating conditions.

A new procedure focused on reliability analysis of subtransmission systems supported by the state enumeration technique is presented. This new methodology is conducted in three stages. First, a classical state enumeration reliability assessment is performed for the branch-node model of a subtransmission system, assuming that substations are perfectly reliable. Second, a detailed model of the subtransmission system is considered and the reliability of each substation is assessed by considering them in a ‘one-by-one’ process, supposing perfect operation for the branch-node model. Finally, the reliability indices calculated in the first and second stages are analytically combined to obtain the reliability indices for the subtransmission system (system reliability indices) and for the load nodes of the distribution system (load-node reliability indices). Test results show that the proposed methodology is suitable for both planning studies and 24 h-ahead security assessment.

Linear programming (LP) techniques and interior point methods (IPMs) have been used extensively for optimal power flow's solution. Both have their merits as well as limitations. There are situations in which a technique which combines the merits of both, is desirable. This study attempts to develop this technique using the well-known compact model, with hitherto unused non-linear approximation, in terms of incremental variables. Sequential applications of this non-linear incremental model, which is solved by using any of the IPM variants, leads to solution of the original problem. It avoids the vexed step-size restrictions, so common in sequential LP techniques. This model also allows for speed-accuracy trade-off. Results for few IEEE test systems and a fictitious 1000 bus system, have been obtained to explore the potential of this technique.

Security constrained optimal power flow (SCOPF) is a key operation function for modern power systems. In this study, a new bi-level optimisation approach is proposed to solve this problem considering a comprehensive SCOPF model, including, for example, valve loading effect, multi-fuel option and prohibited operating zone constraints of thermal units as well as AC network modelling and AC security constraints. Economic dispatch is solved in the lower level of the proposed approach and using its results as the initial solution, SCOPF is solved in the upper level with high convergence rate. For the both levels, a new enhanced gravitational search algorithm is suggested as the optimisation tool. The proposed bi-level approach is tested on 9-bus, IEEE 57-bus, IEEE 118-bus, IEEE 300-bus and polish 2746-bus test systems. Obtained results from the proposed approach for the test cases are compared with the results of other SCOPF solution methods and published literature figures. These comparisons confirm the validity of the developed approach.

This study presents a new two-stage heuristic algorithm for loss-reduction reconfiguration of radial electrical distribution system. In the first stage, which is a close-all-switch technique and based on successively opening the minimum-current branches, the algorithm determines a feasible solution for the switch configuration. The second stage uses a neighbour-chain updating process (NCUP), which periodically updates each open switch of the configuration with its best neighbourhood switches. Depending on applicability of the circular mechanism on NCUP, two methods are utilised. The results of applying the proposed methods on five standard hypothetical or practical test networks demonstrate their excellent performance in obtaining best-known solutions in a systematic manner.

From voltage and current signals it is possible to obtain relevant information for solving some problems in several industrial and scientific applications as power quality (PQ) monitoring, monitoring and diagnosis of electrical machines, electric systems protection and control. At present, the PQ monitoring, measure through a set of PQ indices (PQI), is an important topic for the industrial sector since a poor PQ, characterised by the presence of harmonics in the power line, produces irregular or wrong operation of protection systems, excessive neutral currents in 3-phase four-wire systems, overheating of motors, transformers, capacitor banks and wiring in general. The PQI calculation is performed by many techniques proposed in the literature; however, they do not have either good performance for transient signals or the requirements for satisfying the power standards. This work proposes the assessment of the PQI-based in neural networks for transient or stationary signals in 3-phase power systems without losing the power standard requirements. Besides, this work contributes to the industrial application field by allowing the continuous and online monitoring of the PQI thanks to the field programmable gate array implementation of the proposed methodology.

This study presents a fast real-time algorithm to relieve the overload occurring in a bulk power system. Based on DC power flow formulation, theoretical results are obtained for the overload relief by lines and series capacitors (SCs) switching. Two times of power flow analysis is utilised to identify a candidate list of lines and SCs that can relieve the overload by switching. The operator is thus able to make a mitigation plan based on the candidate list and relieve the overload after a quick validation. Examples are used to verify the theoretical results and the proposed algorithm. In addition, WECC's West wide System Model is used to illustrate the proposed algorithm. Numerical test results show that the proposed algorithm is capable of relieving line overloads in a very fast and effective way, and it can be directly applied in practice.

Transmission systems of power systems face risk of emergency transmission contingency and line outages. Proper optimisation tools are required to schedule generation during emergency conditions and to be able to study the effect of the transmission line outages. This study proposes a successive mixed integer linear programming (MILP) method with fuzzy security constraints for solving the AC Security Constrained Unit Commitment (AC-SCUC) challenge with transmission line outages. A linear formulation of AC-SCUC challenge is created resulting in an MILP model. This MILP model is transformed into a robust fuzzy MILP model that overcomes infeasibility arising from line outages. This fuzzy MILP model is set up and solved successively using MILP technique whereas updating both continuous and integer variables making the proposed algorithm efficient. The proposed method is tested on 6-bus, IEEE 57-bus, and IEEE 118-bus systems to demonstrate its capabilities and benefits.

With the increasing integration of wind farms, modification of current tools for evaluating and managing power systems such as available transfer capability (ATC) becomes an important issue. This study presents a computationally accurate and efficient method in evaluating ATC with large amount of uncertainty based on Latin hypercube sampling (LHS) and scenario clustering techniques. LHS is used in Monte Carlo simulation to select a system state with high sampling efficiency and good precision. Cholesky decomposition is combined into the sampling process to deal with the dependencies among input random variables. The sampled scenarios are clustered by vector quantification clustering algorithm, which contributes to the fast calculation of ATC evaluation for numerous scenarios. Finally, a sensitivity method based on optimal power flow is proposed for the clustered scenarios. The case studies, with the IEEE reliability test system, illustrate the advantages of the proposed method that largely reduces the computation burden under the premise of ensuring its accuracy. The results also verify the obvious enhancement of spatially correlated wind power on the volatility of ATC.

Transient stability constrained optimal power flow (TSCOPF) is an important and difficult problem. When multiple contingencies are considered, a reliable contingency filtering technique should be used to reduce the scale of TSCOPF problem. This study brings in the concepts of active contingency and critical contingency, and develops a novel contingency filtering strategy. Based on time-domain numerical simulations, the proposed contingency filtering strategy first screens all the considered contingencies and identifies active contingencies whose severe indices violate the pre-defined threshold of transient stability, then further finds out the critical contingencies in which some generators are most severely disturbed according to the severe indices trajectories. The severe indices can be such as maximal relative rotor angles, maximal transient generator voltage dips and so on. Taking only the critical contingencies into account, the scale of TSCOPF problem is reduced significantly. Interior point method is used to solve the reduced TSCOPF problem. Numerical results on several cases indicate that the proposed contingency filtering technique is reliable and efficient. Compared with the conventional TSCOPF approach, which involves all the contingencies, the proposed contingency filtering strategy possesses overwhelming advantages in CPU time and memory consumption, and is hopeful to solve TSCOPF problems with many contingencies.