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To operate with high efficiency and minimise the risks of power failures, power systems require careful monitoring. The availability of real-time data is crucial for assessing the performance of the grid and assisting operators in gauging the present security of the grid. Traditional supervisory control and data acquisition (SCADA)-based systems actually employed provides steady-state measurement values which are the calculation premise of State Estimation. More often, however, the power grid operates under dynamic state and SCADA measurements can lead to erroneous and inaccurate calculation results. The introduction of the phasor measurement unit (PMU) which provides real-time synchronised voltage and current phasors with very high accuracy is universally recognised as an important aspect of delivering a secure and sustainable power system. PMUs are a relatively new technology and because of their high procurement and installation costs, it is imperative to develop appropriate methodologies to determine the minimum number of PMUs as well as their strategic placements to guarantee full observability of a power system. Thus, the problem of the optimal PMU placement (OPP) is formulated as an optimisation problem subject to various constraints to minimise the number of PMUs while ensuring complete observability of the grid. In this chapter, integer linear programming (ILP), genetic algorithm (GA) and non-linear programming (NLP) constrained models of the OPP problem are presented. A new methodology is proposed to incorporate several constraints using the NLP. The optimisation methods have been written in Matlab software and verified on the standard Institute of Electrical and Electronics Engineers (IEEE) 14-bus test system to authenticate their effectiveness. This chapter targets United Nations Sustainable Development Goal 7.

This paper aims to find the minimum possible number of phasor measurement units (PMUs) to achieve maximum and complete observability of the power system and improve the redundancy of measurements, in normal cases (with and without zero injection bus [ZIB]), and then in conditions of a single PMU failure and outage of a single line.

An efficient approach operates adequately and provides the optimal solutions for the PMUs placement problem. The finest function of optimal PMUs placement (OPP) should be mathematically devised as a problem, and via that, the aim of the OPP problem is to identify the buses of the power system to place the PMU devices to ensure full observability of the system. In this paper, the grey wolf optimizer (GWO) is used for training multi-layer perceptrons (MLPs), which is known as Grey Wolf Optimizer (GWO) based Neural Network (“GW-NN”) to place the PMUs in power grids optimally.

Following extensive simulation tests with MATLAB/Simulink, the results obtained for the placement of PMUs provide system measurements with less or at most the same number of PMUs, but with a greater degree of observability than other approaches.

The efficiency of the suggested method is tested on the IEEE 14-bus, 24-bus, New England 39-bus and Algerian 114-bus systems.

This paper proposes a new method for placing PMUs in the power grids as a multi-objective to reduce the cost and improve the observability of these grids in normal and faulty cases.

Heuristic algorithms have been widely used in different types of optimization problems. Their unique features in terms of running time and flexibility have made them superior to deterministic algorithms. To accurately compare different heuristic algorithms in solving optimization problems, the final optimal solution needs to be known. Existing deterministic methods such as Exhaustive Search and Integer Linear Programming can provide the final global optimal solution for small-scale optimization problems. However, as the system grows the number of calculations and required memory size incredibly increases, so applying existing deterministic methods is no longer possible for medium and large-scale systems. The purpose of this paper is to introduce a novel deterministic method with short running time and small memory size requirement for optimal placement of Micro Phasor Measurement Units (µPMUs) in radial electricity distribution systems to make the system completely observable.

First, the principle of the method is explained and the observability of the system is analyzed. Then, the algorithm’s running time and memory usage when applying on some of the modified versions of the Institute of Electrical and Electronics Engineers 123-node test feeder are obtained and compared with those of its deterministic counterparts.

Because of the innovative method of step-by-step placement of µPMUs, a unique method is developed. Simulation results elucidate that the proposed method has unique features of short running time and small memory size requirements.

While the mathematical background of the observability study of electricity distribution systems is very well-presented in the referenced papers, the proposed step-by-step placement method of µPMUs, which shrinks unobservable parts of the system in each step, is not discussed yet. The presented paper is directly applicable to typical problems in the field of power systems.

The earlier methods are more resilient to improvements such as load shift and path change. This results in problems such as a voltage drop and a high reactive flux. In addition, due to the delay, congestion or interruption of the transmission, the system cannot receive all phasor measurement unit (PMU) measurements at the relevant time as well as the presence of noise in the received data.

With the development of wide area measurement system technologies, it seems to be possible to track voltage stability online via time-stamped PMUs. As the voltage instability causes a voltage decomposition, voltage instability is one of the most important problems when monitoring the power supply.

This harmonic distortion significantly decreases the data quality in the grid. As a result, instability ascertainment based on PMU has been suggested as a method for detecting voltage instability in power systems monitored with PMU. In addition, a technique called instability amendment via load dropping has been proposed to keep the device from collapsing due to voltage failure.

To improve the power output, the power prominence melioration technique was developed. This proposed system has been implemented in MATLAB Simulink and compared with the recent researches.

This paper aims to report how one can assess the transient stability of a power system by using stacked auto-encoders.

The proposed algorithm works in a power system equipped with the wide area measurement system. To be more exact, it needs pre- and post-disturbance values of frequency sent from phasor measurement units.

The authors have investigated the performance of the proposed method. Going through details, the authors have simulated many contingencies, and then have predicted the transient stability in each of which by using the proposed algorithm.

The results demonstrate that the algorithm is fast, and it has acceptable performance under different circumstances including the change of system topology and failures of telecommunication channels.

The purpose of this paper is to propose an extended Kalman particle filter (EPF) approach for dynamic state estimation of synchronous machine using the phasor measurement unit’s measurements.

EPF combines the extended Kalman filter (EKF) with the particle filter (PF) to accurately estimate the dynamic states of synchronous machine. EKF is used to make particles of PF transfer to the likelihood distribution from the previous distribution. Therefore, the sample impoverishment in the implementation of PF is able to be avoided.

The proposed method is capable of estimating the dynamic states of synchronous machine with high accuracy. The real-time capability of this method is also acceptable.

The effectiveness of the proposed approach is tested on IEEE 30-bus system.

Introducing EKF into PF, EPF is proposed to estimate the dynamic states of synchronous machine. The accuracy of a dynamic state estimation is increased.

Energy production and distribution is undergoing a revolutionary transition with the advent of disruptive technologies such as the Internet of Energy (IoE), 5G and artificial intelligence (AI). IoE essentially involves automating and enhancing the energy infrastructure: the power grid from grid operators to energy generators and distribution utilities. The IoE also relies on powerful connectivity networks such as 5G, big data analytics and AI to optimise its operation. By incorporating the technology that employs ubiquitous devices such as smartphones, tablets or smart electric vehicles, it will be possible to fully exploit the potential of IoE using 5G networks. 5G networks will provide high speed connections between devices such as drones, tractors and cloud networks, to transfer huge amounts of sensor data. Additionally, there are many sources of isolated data across the main energy production units (generation, transmission and distribution), and the data is increasing at phenomenal rates. By applying AI to these data, major improvements can be brought at each stage of the energy production chain. Tying renewable energy to the telecommunications sector and leveraging on the potential of data analytics is something which is gaining major attention among researchers and industry experts. This chapter therefore explores the combination of three of the most promising technologies i.e. IoE, 5G and AI for achieving affordable and clean energy, which is SDG 7 in the UN Sustainable Development Goals (SDGs).

The power systems behavior is nonlinear, and this is reflected in that the measurement signals are composed by multi-components. Thus, this paper aims to present a method for analyzing multi-component signals that allow calculating signal parameters such as frequency, damping constant, amplitude and phase for each component, as well as determining the direct current component.

The method proposed is based in the Z-transform of a damped sinusoidal signal with direct current. Only the Z-transform poles are used to form equation systems which are used to obtain frequency and damping. Then, the amplitude, the phase and the direct current component are determined by the above results.

The method is able to determine frequencies, damping constants, phases and amplitudes of the different modal components of a signal using only a few measurements. Moreover, the method does not require filter banks tuned with some previous knowledge of signal’s characteristics. The presented test cases of field measured signals show the good performance of the proposed method, which is able to obtain the parameters of interest with a very short observation window.

One quality of this method is that it has a very short delay to reach the first solution and from there you get one result each sample; the delay time is equivalent to 2 + 4C samples where C is the number of components in the signal. Finally, it is concluded that because of the small number of samples that are needed and the low algorithmic complexity of the methodology, the method is apt to make applications in real time.