Three-phase state estimation using hybrid particle swarm optimization

This paper compares particle swarm optimization (PSO) techniques for a reactive power allocation planning problem in power systems. The problem can be formulated as a mixed-integer nonlinear optimization problem (MINLP). The PSO based methods determines a reactive power allocation strategy with continuous and discrete state variables such as automatic voltage regulator (AVR) operating values of electric power generators, tap positions of on-load tap changer (OLTC) of transformers, and the number of reactive power compensation equipment. Namely, this paper investigates applicability of PSO techniques to one of the practical MINLPs in power systems. Four variations of PSO: PSO with inertia weight approach (IWA), PSO with constriction factor approach (CFA), hybrid particle swarm optimization (HPSO) with IWA, and HPSO with CFA are compared. The four methods are applied to the standard IEEE14 bus system and a practical 112 bus system.

This paper has discussed transformer tap position estimation with continuous and discrete variables in the context of three-phase distribution state estimation (SE). Ordinal optimization (OO) technique has been applied to estimate the transformer tap position for the first time in an unbalanced three-phase distribution network model. The results on 129 bus system model have demonstrated that the OO method can generate a reliable estimate for transformer exact tap position with discrete variables in distribution system SE and also in short period of time. In this paper, the node voltages and power losses are calculated for 129 bus network. It is also demonstrated that OO is much faster than other accurate methods such hybrid particle swarm optimization. The losses obtained with OO are much accurate. In view of this, OO performs better than weighted least square as it provides higher accuracy of the loss calculation. In a distribution network where about 5-6% of electricity generated is lost, accurate estimation of this loss has significant technical and commercial value. The authors believe the technique proposed will help realize those benefits.

This paper proposes a new multiobjective hybrid particle swarm optimization (PSO)–krill herd (KH) Pareto-based optimization algorithm to optimize number and location of the measurement devices for accurate state estimation (SE) in smart distribution networks. Three objectives are considered to be minimized: 1) the total cost; 2) the average relative percentage error (APE) of bus voltage magnitude; and 3) APE of bus voltage angle. As the objective functions are conflicting with respect to each other, a multiobjective Pareto-based nondominated sorting hybrid PSO–KH optimization algorithm is proposed. In this approach, the random variation in loads and the metrological error of the measurement devices are also taken into account. The proposed algorithm minimizes the cost and enhances the accuracy of the distribution state estimator for better monitoring and control of the system. Furthermore, the impacts of distributed generation on SE performance are also investigated. The feasibility of the proposed algorithm is demonstrated on IEEE 69-bus system and practical Indian 85-bus radial distribution network. The results obtained are compared with conventional KH algorithm and PSO, with well-known multiobjective nondominated sorting genetic algorithm and also with an existing technique based on dynamic programming method for validation.

The measurements given by Supervisory Control And Data Acquisition (SCADA) systems are generally insufficient for Distribution network State Estimation (DSE). The accuracy of DSE seriously relies on the pseudo measurements, which are generally inaccurate. In historical scene, measurements given by smart distribution transformer meter (SDTM) can be used to enrich the measurements for DSE. The hybrid measurements of SCADA and SDTM can satisfy the observability of DSE with certain redundancy. By using rectangular coordination and introducing intermediate variables and quadratic equalities, the SCADA/SDTM hybrid DSE is modeled as quadratic constrained quadratic estimation (QCQE) problem, which can be solved by traditional state estimation methods with sparse techniques efficiently and conveniently. Simulations on the IEEE 33-bus system show the accuracy of the hybrid DSE. The proposed DSE has been implemented in distribution networks in Zhejiang and shows good efficiency and accuracy.

Vibration measurement has been used for power transformer's On-Load Tap Changer (OLTC) condition monitoring. In this paper, a signal processing method based on Savitzky-Golay filter is developed. The proposed method is applied to process a series of vibration signals measured from an OLTC when the OLTC operates throughout a number of its tap positions and subsequently evaluate the condition of these tap positions. Savitzky-Golay filter extracts the information embedded in the originally measured signal without latency in time. As such, it transforms effectively complicate feature of vibration signal into a simplified form of envelope curve. After being processed by Savitzky-Golay filter, the vibration signals corresponding to different tap positions are compared. The comparison results help to understand the characteristics of vibration signals for monitoring the mechanical condition change in OLTC.

State estimation has an important role in the operation of power systems and associated power markets. It is well known that state estimators are vulnerable to any existing critical measurements since their errors cannot be detected. Therefore, by manipulating a finite number of critical measurements, a hacker can bias the results of state estimation without being detected. Those biased results include both system states (bus voltage magnitudes and angles) and branch flows that are calculated by using the system states. This work investigates the cyber-attacks carried out by strategically manipulating critical measurements and proposes a method to identify the set of estimated flows that are vulnerable to those cyber-attacks. The proposed method will be described and simulations will be presented to validate the method. The paper considers power systems that are measured by a mixed set of measurements, namely power flow, injection and voltage magnitude measurements, as well as phasor measurements provided by the phasor measurement units (PMUs).

In this paper a new approach to state estimation in distribution networks is proposed. This approach is more robust against large uncertainties of the state estimation inputs than the conventional method. Traditionally, the goal of state estimation was to estimate the exact value of network parameters, such as voltages and currents. This works well in transmission networks where many real time measurements are available. In distribution networks, however, only few real-time measurements are available. This means that the estimated state can be significantly different from the actual network state. Therefore, the focus of the proposed robust state estimation is shifted from estimating the exact values of the network parameters to the confidence that these parameters are within their respective constraints. This approach is able to provide useful results for distribution network operation, even if large uncertainties are present in the estimated network state.

Cyber-physical systems (CPS) maintain operation, reliability, and safety performance using state estimation and control methods. Internet connectivity and Internet of Things (IoT) devices are integrated with CPS, such as in smart grids. This integration of Operational Technology (OT) and Information Technology (IT) brings with it challenges for state estimation and exposure to cyber-threats. This research establishes a state estimation baseline, details the integration of IT, evaluates the vulnerabilities, and develops an approach for detecting and responding to cyber-attack data injections. Where other approaches focus on integration of IT cyber-controls, this research focuses on development of classification tools using data currently available in state estimation methods to quantitatively determine the presence of cyber-attack data. The tools may increase computational requirements but provide methods which can be integrated with existing state estimation methods and provide for future research in state estimation based cyber-attack incident response. A robust cyber-resilient CPS includes the ability to detect and classify a cyber-attack, determine the true system state, and respond to the cyber-attack. The purpose of this paper is to establish a means for a cyber aware state estimator given the existence of sub-erroneous outlier detection, cyber-attack data weighting, cyber-attack data classification, and state estimation cyber detection.

A method for determining the grid state of a medium voltage electrical power grid with incomplete measurements is proposed. It is based on estimating the vector of active and reactive power through voltage magnitude and angle measurements with an Iterated Extended Kalman Filter for a simplified dynamic model of the grid in quasi-stationary conditions. Through rough forecasts of load and generation data, it is possible to get an accurate voltage magnitude and angle estimate even for buses without any measurement. Simulation results are obtained for a UKGDS network model.

Decoupled State-Estimation in Energy Control Centres

Number of distributed generation (DG) plants integrated to utility network increases due to rising of electricity consumption. DG plants have a big impact on voltage level of network in accordance with this increasing. Especially DG plants based on renewable resources may cause voltage rise and drop because of their intermittently energy generation. Generally, the voltage level is regulated by reactive power control technique and On-load tap changer (OLTC). Generally, HV/MV transformers use OLTC to regulate the voltage level under load. The conventional voltage control techniques depend on current of branch at the main substation end. If the rising of DG installation is taken into consideration, the conventional methods can be inadequate. Artificial intelligence techniques can be used to mitigate this problem. In this paper, an Artificial Neural Networks (ANN) model is designed for determination of OLTC best tap position. ANN model is trained by using different values of network voltage level, DG generation values and amount of load demand. Designed model is applied on electrical network of Selcuk University. The electrical network of Selcuk University is modeled by using PSCAD/EMTDC. 1MW Photovoltaic (PV) power plant is determined as DG plant.

The AC-DC hybrid microgrid is a credible evolution path for the microgrid. State estimation in complex distribution network is a significant foundation for the safe operation. In AC-DC hybrid microgrid, centralised state estimation algorithms have quite a few deficiencies such as large communication capacity and privacy protection. In response to these obstacles, this paper proposed a novel distributed state estimation algorithm. First, a three-stage state estimation model was established for AC-DC hybrid microgrid, in which the second stage is a nonlinear transform for the linearisation of the nonlinear state estimation progress so that the first and third stage can solve the linear state estimation problem. Furthermore, the Lagrangian relaxation method was ultilised for the distributed problem solution. In the proposed algorithm, AC subsystems and DC subsystems respectively execute state estimation through local information, and the overall consistency of the system state estimation can be eventually achieved according to the transfer iteration of the boundary information. The computational efficiency and convergence of the proposed method was ensured owing to the combination of bilinear theory and Lagrangian relaxation. Simulation results indicate that the proposed method can achieve its goal and be superior to existing method in efficiency.

A final focus model for heavy-ion fusion driver system codes

Grid-level battery energy storage systems have been proposed as a technology that can mitigate the adverse effects of stochastic renewable energy sources by performing electric energy time shift on the grid. In this work, full-year hourly AC optimal power flow simulations are supplemented by a battery energy storage scheduling routine that utilises multi-period optimal power flow simulations, with dispatch costs based on battery degradation. Different penetration and cost scenarios for the 2030 central European transmission grid are investigated. It is found that increasing battery energy penetrations lead to a lower overall cost of electricity and significantly less wind and solar photovoltaic power curtailment, but that the capacity factors of battery energy storage systems also decrease considerably. The utilisation of battery energy storage systems is also highly dependent on geographic location, with systems located in Austria, southern and western Germany achieving relatively higher capacity factors.

Microgrids enable fast electrification of rural communities. Affordability of these community microgrids is essential for their sustainability and effective utilisation of the services they offer. This paper addresses some of the critical elements that have to be considered when designing microgrids for rural communities. The results obtained show that with the right choice of batteries and their specifications and availability of anchor customers the overall cost of electricity can be reduced.