Quality problems encountered in power systems usually originate from reactive and harmonic current components drawn by non-linear loads. Therefore the extraction of reactive and harmonic current components from distorted current waveform is a vital issue in the works aiming to monitor and eliminate power quality problems. In this study, a new method that provides the extraction of active, reactive and harmonic current components from distorted current is presented. The method has been based on the differential equation of the single-phase source-load system. The proposed method performs computations by using a sliding sampling window that has a width of half of the fundamental cycle. Immunity to noise, being less affected from frequency deviations and being not influenced from dc component that may exist in load currents are some other advantages of the proposed method. The method has been analysed and compared to two commonly used methods, namely discrete Fourier transform (DFT) and dq, by using a series of simulations that have been executed in Matlab/Simulink environment. Simulation results demonstrated the effectiveness of the proposed method.

Wind energy is intermittent and uncertain. This uncertainty creates additional risk in the day-ahead 24-h dispatch schedule. Wind speed can be forecasted for the next 24-h and hourly power forecasts can be best described using probabilistic models. Security and risk constrained probabilistic unit commitment (SRCPUC) algorithms considering probabilistic forecast models of wind power can be used to optimally schedule conventional and wind generation to minimise the total cost and minimise risk. However, inclusion of non-linear probabilistic forecast models in a SRCPUC algorithm is computationally very challenging. In this study, the proposed SRCPUC algorithm uses a triangular approximate distribution (TAD) model to probabilistically represent power output of wind generator. The TAD model quantifies hourly potential risk because of expected energy not served (EENS) from uncertain wind power. Reserves are optimally scheduled to counter EENS. Total energy cost, reserve cost and risk from EENS are minimised in the proposed SRCPUC algorithm. The proposed algorithm is implemented on 6-bus and 118-bus IEEE systems. The results are compared with classical enumeration technique. Significant benefits in computing time (more than 500 times faster) are seen while the numerical results are observed to be highly accurate.

A novel fuzzy proportional–integral derivative (PID) controller is proposed in this study for automatic generation control (AGC) of interconnected power systems. The optimum gains of the proposed fuzzy PID controller are optimised employing a hybrid differential evolution particle swarm optimisation (DEPSO) technique using an integral of time multiplied by absolute value of error criterion. The superiority of hybrid DEPSO algorithm over differential evolution and particle swarm optimisation (PSO) algorithm has also been demonstrated. The results are also compared with some recently published approaches such as artificial bee colony and PSO based proportional–integral/PID controllers for the same interconnected power systems. Furthermore, performance of the proposed system is analysed by varying the different parameters such as loading condition, system parameters and objective functions. It is observed that the optimum gains of the proposed fuzzy PID controller need not be reset even if the system is subjected to variation in loading condition and system parameters. Finally, the study is extended to a three area system considering generation rate constraint to demonstrate the ability of the proposed approach to cope with multiple interconnected systems. Comparison with previous AGC methods reported in the literature validates the significance of the proposed approach.

This study aims to solve for the optimal location and capacity of distributed generation (DG), taking vulnerable node identification into consideration, in active distribution networks (ADN). Vulnerable nodes exist in the distribution network. Considering that the power fluctuations of those vulnerable nodes will have a significant impact on other nodes, the allocations of the DGs should avoid such nodes. Therefore vulnerable node identification and removal from the network can greatly limit the siting range of DGs. In this study, the vulnerable nodes are identified based on the small-world network theory, which is used as the preliminary location of the DG. Then, a genetic algorithm (GA) is proposed to finally address the optimal location and capacity for grid-connected DG. A GA with voltage boundary constraints is utilised to effectively prevent the bus voltage from reaching its boundary. This method improves the calculation efficiency greatly and is therefore suitable for flexible distribution network topology in ADN. According to the change of the distribution network topology, the corresponding optimal location and capacity limit for the DG can be quickly calculated. Some examples validate the algorithm and prove that it has fast convergence.

This study presents the basic features of a Monte Carlo approach for reliability evaluation of distribution systems without embedded generation using parallel computation. The test system is represented by means of a three-phase model that includes protective devices and is simulated during one year. The procedure has been implemented in an open environment in which OpenDSS is driven from MATLAB. The document includes a detailed description of the procedure, and some results derived from the simulation of an overhead distribution test system.

This study proposes a methodology to assess the effect of wind power production on the flexibility requirements of a power system. First, the study describes the probabilistic characteristics of the intra-hour net load variability and demonstrates that they are best captured by non-parametric statistics. Then, this non-parametric approach is used to determine simultaneously the hourly flexibility requirements at a given probability level for large and small, continuous and discrete disturbances. This approach allocates the required flexibility among primary, secondary and tertiary regulation intervals. The usefulness of this method is then illustrated using actual 1 min resolution net load data, which has been clustered to take advantage of seasonal and daily differences in flexibility requirements.

This study presents a comprehensive approach to tackle the problem of optimal placement and coordinated tuning of power system supplementary damping controllers (OPCTSDC). The approach uses a recursive framework comprising probabilistic eigenanalysis (PE), a scenario selection technique (SST) and a new variant of mean-variance mapping optimisation algorithm (MVMO-SM). Based on probabilistic models used to sample a wide range of operating conditions, PE is applied to determine the instability risk because of poorly-damped oscillatory modes. Next, the insights gathered from PE are exploited by SST, which combines principal component analysis and fuzzy c-means clustering algorithm to extract a reduced subset of representative scenarios. The multi-scenario formulation of OPCTSDC is then solved by MVMO-SM. A case study on the New England test system, which includes performance comparisons between different modern heuristic optimisation algorithms, illustrates the feasibility and effectiveness of the proposed approach.

Cascading failures can easily occur and cause a major blackout in power grid when an important node breaks down. It is an essential problem to evaluate the importance of nodes in power system planning and operation. In this study, a method for evaluating the importance of power grid nodes based on PageRank (PR) algorithm is proposed. First, according to the comparison of the internet and power grid topology, a directed graph is established. Second, based on the directed graph, an index is proposed to estimate the importance of power grid nodes based on PR algorithm. Then, according to the characteristics of power grid, a modified algorithm, which takes the importance of nodal load, nodal load capacity and network topology into account, is proposed. Finally, case study shows the necessity of considering the factors to evaluate the importance of power grid nodes and the effective of index and algorithm to identify critical nodes in this study.

The continuous expansion of consumer-driven installations of residential rooftop photovoltaic (PV) systems causes serious power quality, notable voltage variations and unbalance, which limit the number and capacity of the further connections. The latent reactive power capability of PV inverters can increase the network PV hosting capability. This study proposes both reactive power control and real power curtailment as a comprehensive inverter control strategy to improve the operating performance of unbalanced three-phase four-wire low voltage distribution networks with high penetrations of residential PV systems. A multi-objective optimal power flow (OPF) problem that can simultaneously improve voltage magnitude and balance profiles while minimising network loss and generation costs is defined. The solution is found using the global solver based on Sequential Quadratic Programming algorithm with multiple starting points in Matlab. Detailed simulations are performed and analysed for typical operating scenarios over 24-h period on a real three-phase four-wire unbalanced distribution network in Perth Solar City trial, Australia. Smart meter readings are used to justify the accuracy and validation of the network model and the proposed multi-objective OPF model.

It is an essential element to estimate the state of system loads in the emerging smart grid system. This study is focused on finding a distribution of power load profile through a Gaussian mixed model. The authors propose a modified variational Bayesian inference (VBI) method to set parameters of the model. Compared with other approximation techniques, such as maximum likelihood, expectation maximum (EM) algorithm, they show that the author's Bayesian approaches can solve the problem of data singularity and the problem of over-fitting. Utilising the weights of components, their modified algorithm can dynamically remove some redundant components during iterations. The presented method in this study also reduces the computational complexity and thus improves the speed of iteration. They use practical data to examine EM algorithm, VBI algorithm and their improved VBI algorithm. The experiments verify the advantages of Bayesian inference approaches and the validity/reliability of the proposed algorithm. The performance of the algorithms is also demonstrated by the data from PJM Company.

This study presents an analytical approach for finding the possible impacts of flexible AC transmission system (FACTS) devices such as unified power flow controller (UPFC) and off-shore wind penetration on distance relay characteristics. Generating relay tripping characteristics is one of the challenging issues for transmission line distance relays as the present day transmission systems is subjected to a more stressed environment with respect to power system operation. Moreover, inclusion of FACTS devices such as UPFC in the transmission system seriously impacts the performance of the distance relays as the apparent impedance changes. Furthermore, the reach setting of the relay is significantly affected as the relay end voltage fluctuates continuously when off-shore wind-farms are connected to power transmission systems. Thus, generating tripping characteristics for appropriate operating conditions is a demanding concern and the same has been addressed in the proposed research work. Extensive testing is carried out in generating relay tripping characteristics with possible variations under the operating conditions of UPFC and wind-farm and it is observed that the characteristics are significantly affected for different operating conditions. Furthermore, the performance of the derived relay characteristics is validated on possible transmission line fault events and is found to work effectively.

This study addresses clarification regarding total payment function calculation in reactive power market. One of the main sources of reactive power in transmission network is synchronous generator. Capability curves of reactive power produced by synchronous generators are surveyed in this study. Meanwhile, using these curves, all the costs of one synchronous generator to inject or absorb reactive power are calculated. These costs include an availability cost and cost of losses as well as cost of losing the sale opportunity of electrical energy because of production or absorption of reactive power. The IEEE 24-bus reliability test system is used to illustrate the proposed cost calculation technique in reactive power market.