Phasor Parameter Modeling and Time-Synchronized Calculation for Representation of Power System Dynamics

The electric power grid is undergoing sustained disturbances. In particular, the extreme dynamic events disrupt normal electric power transfer, degrade power system operating conditions, and may lead to catastrophic large-scale blackouts. Accordingly, control applications are deployed to detect the inception of extreme dynamic events, and mitigate their causes appropriately, so that normal power system operating conditions can be restored. In order to achieve this, the operating conditions of the power system should be accurately characterized in terms of the electrical quantities that are crucial to control applications. Currently, the power system operating conditions are obtained through SCADA system and the synchrophasor system. Because of GPS time-synchronized waveform sampling capability and higher measurement reporting rate, synchrophasor system is more advantageous in tracking the extreme dynamic operating conditions of the power system. In this Dissertation, a phasor parameter calculation approach is proposed to accurately characterize the power system operating conditions during the extreme electromagnetic and electromechanical dynamic events in the electric power grid. First, a framework for phasor parameter calculation during both electromagnetic and electromechanical dynamic events is proposed. The framework aims to satisfy both P-class and M-class PMU algorithm design accuracy requirements with a single algorithm. This is achieved by incorporating an adaptive event classification and algorithm model switching mechanism, followed by the phasor parameter definition and calculation tailored to each identified event. Then, a phasor estimation technique is designed for electromagnetic transient events. An ambient fundamental frequency estimator based on UKF is introduced, which is leveraged to adaptively tune the DFT-based algorithm to alleviate frequency leakage. A hybridization algorithm framework is also proposed, which further reduces the negative impact caused by decaying DC components in electromagnetic transient waveforms. Then, a phasor estimation technique for electromechanical dynamics is introduced. A novel wavelet is designed to effectively extract time-frequency features from electromechanical dynamic waveforms. These features are then used to classify input signal types, so that the PMU algorithm modeling can be thereafter tailored specifically to match the underlying signal features for the identified event. This adaptability of the proposed algorithm results in higher phasor parameter estimation accuracy. Finally, the Dissertation hypothesis is validated through experimental testing under design and application test use cases. The associated test procedures, test use cases, and test methodologies and metrics are defined and implemented. The impact of algorithm inaccuracy and communication network distortion on application performance is also demonstrated. Test results performance is then evaluated. Conclusions, Dissertation contributions, and future steps are outlined at the end

Power system events classification using genetic algorithm based feature weighting technique for support vector machine

Abstract: Currently, ensuring that power systems operate efficiently in stable and secure conditions has become a key challenge worldwide. Various unwanted events including injections and faults, especially within the generation and transmission domains are major causes of these instability menaces. The earlier operators can identify and accurately diagnose these unwanted events, the faster they can react and execute timely corrective measures to prevent large-scale blackouts and avoidable loss to lives and equipment. This paper presents a hybrid classification technique using support vector machine (SVM) with the evolutionary genetic algorithm (GA) model to detect and classify power system unwanted events in an accurate yet straightforward manner. In the proposed classification approach, the features of two large dimensional synchrophasor datasets are initially reduced using principal component analysis before they are weighted in their relevance and the dominant weights are heuristically identified using the genetic algorithm to boost classification results. Consequently, the weighted and dominant selected features by the GA are utilized to train the modelled linear SVM and radial basis function kernel SVM in classifying unwanted events. The performance of the proposed GA-SVM model was evaluated and compared with other models using key classification metrics. The high classification results from the proposed model validates the proposed method. The experimental results indicate that the proposed model can achieve an overall improvement in the classification rate of unwanted events in power systems and it showed that the application of the GA as the feature weighting tool offers significant improvement on classification performances

Optimization for Decision Making II

In the current context of the electronic governance of society, both administrations and citizens are demanding the greater participation of all the actors involved in the decision-making process relative to the governance of society. This book presents collective works published in the recent Special Issue (SI) entitled “Optimization for Decision Making II”. These works give an appropriate response to the new challenges raised, the decision-making process can be done by applying different methods and tools, as well as using different objectives. In real-life problems, the formulation of decision-making problems and the application of optimization techniques to support decisions are particularly complex and a wide range of optimization techniques and methodologies are used to minimize risks, improve quality in making decisions or, in general, to solve problems. In addition, a sensitivity or robustness analysis should be done to validate/analyze the influence of uncertainty regarding decision-making. This book brings together a collection of inter-/multi-disciplinary works applied to the optimization of decision making in a coherent manner

Multi particle swarm optimisation algorithm applied to supervisory power control systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonPower quality problems come in numerous forms (commonly spikes, surges, sags, outages and harmonics) and their resolution can cost from a few hundred to millions of pounds, depending on the size and type of problem experienced by the power network. They are commonly experienced as burnt-out motors, corrupt data on hard drives, unnecessary downtime and increased maintenance costs. In order to minimise such events, the network can be monitored and controlled with a specific control regime to deal with particular faults. This study developed a control and Optimisation system and applied it to the stability of electrical power networks using artificial intelligence techniques. An intelligent controller was designed to control and optimise simulated models for electrical system power stability. Fuzzy logic controller controlled the power generation, while particle swarm Optimisation (PSO) techniques optimised the system’s power quality in normal operation conditions and after faults. Different types of PSO were tested, then a multi-swarm (M-PSO) system was developed to give better Optimisation results in terms of accuracy and convergence speed.. The developed Optimisation algorithm was tested on seven benchmarks and compared to the other types of single PSOs. The developed controller and Optimisation algorithm was applied to power system stability control. Two power electrical network models were used (with two and four generators), controlled by fuzzy logic controllers tuned using the Optimisation algorithm. The system selected the optimal controller parameters automatically for normal and fault conditions during the operation of the power network. Multi objective cost function was used based on minimising the recovery time, overshoot, and steady state error. A supervisory control layer was introduced to detect and diagnose faults then apply the correct controller parameters. Different fault scenarios were used to test the system performance. The results indicate the great potential of the proposed power system stabiliser as a superior tool compared to conventional control systems

Power quality improvement in low voltage distribution network utilizing improved unified power quality conditioner.

Doctoral Degree. University of KwaZulu-Natal, Durban.The upgrade of the power system, network, and as it attained some complexity level, the voltage related problems and power loss has become frequently pronounced. The power quality challenges load at extreme end of the feeder like voltage sag and swell, and power loss at load centre due to peak load as not received adequate attention. Therefore, this research proposes a Power Angle Control PAC approach for enhancing voltage profile and mitigating voltage sag, voltage swell, and reduced power loss in low voltage radial distribution system (RDS). The amelioration of voltage sag, voltage swell, weak voltage profile, and power loss with a capable power electronics-based power controller device known as Improve Unified Power Quality Conditioner I-UPQC was conceived. Also, the same controller was optimally implemented using hybrid of genetic algorithm and improved particle swarm optimization GA-IPSO in RDS to mitigate the voltage issues, and power loss experienced at peak loading. A new control design-model of Power Angle Control (PAC) of the UPQC has been designed and established using direct, quadrature, and zero components dq0 and proportional integral (PI) controller method. The simulation was implemented in MATLAB/Simulink environment. The results obtained at steady-state condition and when the new I-UPQC was connected show that series inverter can participate actively in ameliorating in the process of mitigating sag and swell by maintaining a PAC of 25% improvement. It was observed that power loss reduced from 1.7% to 1.5% and the feeder is within the standard limit of ±5%. Furthermore, the interconnection of I-UPQC with photovoltaic solar power through the DC link shows a better voltage profile while the load voltage within the allowable range of ±5% all through the disturbance and power loss reduction is 1.3%. Lastly, results obtained by optimal allocation of I-UPQC in RDS using analytical and GA-IPSO show that reactive power injection improved the voltage related issues from 0.952 to 0.9989 p.u., and power loss was further reduced to 1.2% from 3.4%. Also, the minimum bus voltage profile, voltage sag, and power loss are within statutory limits of ±5 % and less than 2 %, respectively. The major contributions of this research are the reduction of sag impact and power loss on the sensitive load in RDS feeder.Publications on page iii

Applications of Computational Intelligence to Power Systems

In power system operation and control, the basic goal is to provide users with quality electricity power in an economically rational degree for power systems, and to ensure their stability and reliability. However, the increased interconnection and loading of the power system along with deregulation and environmental concerns has brought new challenges for electric power system operation, control, and automation. In the liberalised electricity market, the operation and control of a power system has become a complex process because of the complexity in modelling and uncertainties. Computational intelligence (CI) is a family of modern tools for solving complex problems that are difficult to solve using conventional techniques, as these methods are based on several requirements that may not be true all of the time. Developing solutions with these “learning-based” tools offers the following two major advantages: the development time is much shorter than when using more traditional approaches, and the systems are very robust, being relatively insensitive to noisy and/or missing data/information, known as uncertainty

Flexitranstore

This open access book comprises 10 high-level papers on research and innovation within the Flexitranstore Project that were presented at the FLEXITRANSTORE special session organized as part of the 21st International Symposium on High Voltage Engineering. FLEXITRANSTORE (An Integrated Platform for Increased FLEXIbility in smart TRANSmission grids with STORage Entities and large penetration of Renewable Energy Sources) aims to contribute to the development of a pan-European transmission network with high flexibility and high interconnection levels. This will facilitate the transformation of the current energy production mix by hosting an increasing share of renewable energy sources. Novel smart grid technologies, control and storage methods, and new market approaches will be developed, installed, demonstrated, and tested introducing flexibility to the European power system. FLEXITRANSTORE is developing a next-generation Flexible Energy Grid (FEG) that will be integrated into the European Internal Energy Market (IEM) through the valorization of flexibility services. This FEG addresses the capabilities of a power system to maintain continuous service in the face of rapid and large swings in supply or demand. As such, a wholesale market infrastructure and new business models within this integrated FEG must be upgraded for network players, and offer incentives for new ones to join, while at the same time demonstrating new business perspectives for cross-border resource management and energy trading

Data-driven short-term load forecast method and demand-side management for distribution network

With the development of the power grid, the smart grid makes the system more intelligent, efficient, sustainable and reliable with integrated Information and Communication Systems. Moreover, the data from the advanced system provides chances to utilise machine learning algorithms to improve the system operation further. In addition, the load profile is undergoing altering and becoming more unpredictable because of the increase in smart home appliances, EVs, e-heating systems, energy storage devices, etc. These factors bring more challenges and opportunities for the future power system to improve operational efficiency and demand response quality. In this regard, considering load forecasting is crucial in smart distribution networks for utility companies, especially those employing the demand-side management alternatives, the short-term load forecast could be more accurate and robust, evolve for future load forecasting purposes, and reveal its value in improving demand-side management qualities. This research proposes a novel Dynamic Adaptive Compensation-Long Short-Term Memory (DAC-LSTM) forecast method. This method uses high time-resolution datasets and LSTM networks as fundamental to give short-term time-series load forecast results. The proposed method dynamically distinguishes the peak and off-peak hours and improves the forecast accuracy separately. For DNOs, the forecast errors, especially during peak hours, lead to penalties to start/stop backup generations or adjust the distribution schedules, and this will result in more operational costs. Further, the proposed method introduces a novel DAC block to compensate for forecast errors according to the error trend calculated by historical forecast and actual load, then applying dynamic adaptive parameters. The greater the current-to-average forecast error ratio or the closer the forecast step to the present time stamp, the larger the compensation factors. Besides, the factor caps are set to prevent the model from over-compensation conditions. The sensitivity of introduced parameters is analysed, providing the performance of the developed method under different parameter values. Afterwards, the proposed method is evaluated with six case studies, including varying the forecast steps (compared with LSTM and ARIMA), limiting the size and length of the training datasets (compared with ARIMA and Persistence), comparing with other state-of-art methods qualitatively, and comparing with ELEXON UK domestic load forecast results. Finally, the advantages of the DAC-LSTM method are validated, including providing accurate short-term load forecast results during peak and off-peak simultaneously, with a shorter length of or fewer households’ historical datasets, and compared with existing transmission network forecast methods. The system operators, like DNOs, can reduce the operational cost with more accurate forecasts during peak hours as well as own more load curtailment potentials during the off-peak hours. Additionally, more contributions, including the future bottom-up load scenarios establishment and the improved Stackelberg Game demand response for end-user utility bill reductions, will help system operators develop suitable DSM alternatives and tariffs based on more realistic and accurate analysis. To be more specific, first, based on the Ten Year Network Development Plan (TYNDP) 2018 and the UK government reports, bottom-up load profiles are designed and generated for the UK distribution network for the scenario years 2020, 2030 and 2040. The DAC-LSTM method is evaluated with these scenario profiles, yielding up to 0.989 and 3.79% (measured in R2 and MAPE) forecast accuracy, for various levels of electric vehicle and e-heating penetration when compared with the ARIMA and Persistence methods. Second, a DSM alternative is built based on a Stackelberg Game to reduce the consumers’ utility bill, which considered the forecast error as a constraint. In this game, given that the consumer offers maximum controllable power to the operator, the game achieves the Stackelberg Equilibrium while maximising the operator’s revenue and supplying the necessary power to consumers. The case study demonstrates that when considering forecast errors in demand response strategies, higher forecast accuracies reduce the electricity bill up to 10.4% in an ideal circumstance. The improved Stackelberg Game makes the forecast error one primary constraint that most existing DSM alternatives lack. This proves the value of utilising state-of-art forecast methods in the deployment of DSM alternatives

Advanced Signal Processing Techniques Applied to Power Systems Control and Analysis

The work published in this book is related to the application of advanced signal processing in smart grids, including power quality, data management, stability and economic management in presence of renewable energy sources, energy storage systems, and electric vehicles. The distinct architecture of smart grids has prompted investigations into the use of advanced algorithms combined with signal processing methods to provide optimal results. The presented applications are focused on data management with cloud computing, power quality assessment, photovoltaic power plant control, and electrical vehicle charge stations, all supported by modern AI-based optimization methods