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The KC-135R Stratotanker is a multifunction slim body aircraft that provides air refueling and airlift for the United States’ war and peacetime requirements, which demand a certain level of availability. As the KC-135R fleet ages, the aircraft availability (AA) rate degrades due to high demand use, stress, and the age of the equipment. Preventative and corrective maintenance is designed to return the aircraft to an available state to meet mission requirements, but the United States Air Force continues to fail at meeting every requirement communicated by commanders for KC-135R air refueling and airlift. Focusing on aircraft metrics can enable a prediction model of AA and provide the unit commanders the tools for data influenced decisions. The analysis of historical aircraft maintenance and flight metrics will show a correlation between tracked metrics and AA, thus, the ability to predict a future availability rate. Furthermore, analyzing the data with machine learning techniques will improve prediction accuracy by evaluating the variable importance and will make inferences learned from mining the data that are otherwise difficult to model in a complex system of systems.

Renewable generation on the electric power grid is expected to increase in prevalence, but once this technology reaches a certain level of penetration, the grid will not be able to handle the variability and intermittency of the generation without the inclusion of energy storage systems. Simultaneous with this increase in renewables with energy storage, the incidence of disturbance events such as extreme weather that cause significant faults and failures on the power grid is also expected to increase. This research optimizes the architecture of energy storage systems on the electrical power grid for resilience to faults caused by extreme disturbance events under a high penetration scenario for rooftop photovoltaic generation. A grid fault model was applied to simulated generation and demand data to determine the application of storage on the grid that maximizes grid resilience to faults. Failures were simulated at multiple locations on the grid model and combinations of storage architectures were applied to determine the application strategy of energy storage that maximized the resilience of the grid.

This paper develops models for hurricane exposure, fragility curve-based damage to electrical transmission grid components and power generating stations using Monte-Carlo simulation, and restoration cost to predict resiliency factors including power generation capacity lost and the restoration cost for electrical transmission grid and power generation system damages. Synthetic grid data are used to model the Energy Reliability Council of Texas (ERCOT) electrical grid. A case study is developed based on Hurricane Harvey. This work is extended to evaluate the changes to resiliency as the percentage of renewable sources is increased from 2017 levels to levels corresponding to the NREL Futures Study 2050 Texas scenarios for 50% and 80% renewable energy.

Logistic regression models can serve as important tools in developing a framework to establish the value of electrical energy storage systems (ESSs). This study provides models that aggregate use-case scenarios of five battery types, as well as pumped hydro-electric storage systems. The grid applications include: bulk energy at generation, auxiliary services at transmission and distribution, and end-use customer services at distributed generation. The data is derived from 1,261 real world systems. Five different models were developed for short, medium, and long-duration grid services. The models are designed to be technology agnostic and are not sensitive to either performance characteristics or operating conditions of the ESS. The results indicate the probability that an energy storage project will provide an individual service use case given that it may also yield another service, and how technology types and multiple selected applications influence those probabilities.

In the United States, legislative and regulatory requirements are the primary drivers for the use of grid-level energy storage. In some cases, state-level legislative mandates called Renewable Portfolio Standards (RPSs) necessitate the use of storage to support renewable generation (e.g., solar, wind energy). At the federal level, the Federal Energy Regulatory Commission (FERC) has issued final Orders that stipulate fair and equitable competition rules for regional interstate transmission markets. In both instances, it is the investor-owned utility (IOU) entities that are financially responsible to either satisfy the state and FERC Orders or face noncompliance fines or tariffs. Unfortunately, utility investors do not have a reliable tool to assist in understanding the front-end installation costs or whole life cycle costs for electrical storage systems that service the electric grid. This paper proposes the use of multiple linear regression (MLR) techniques using R-Script to predict the total capital cost (TCC) and life cycle cost (LCC) of real-world energy storage systems (ESSs) derived from manufactures' design specifications and intrinsic characteristics of lead-acid, lithium-ion, sodium sulfur; and vanadium and Ainc-based Aow Aatteries.

Battery storage is usually employed in photovoltaic (PV) system to mitigate the power fluctuations due to the characteristics of PV panels and solar irradiance. Control schemes for PV-battery systems must be able to stabilize the bus voltages as well as to control the power flows flexibly. This paper proposes a comprehensive control and power management system (CAPMS) for PV-battery-based hybrid microgrids with both ac and dc buses, for both grid-connected and islanded modes. The proposed CAPMS is successful in regulating the dc and ac bus voltages and frequency stably, controlling the voltage and power of each unit flexibly, and balancing the power flows in the systems automatically under different operating circumstances, regardless of disturbances from switching operating modes, fluctuations of irradiance and temperature, and change of loads. Both simulation and experimental case studies are carried out to verify the performance of the proposed method.

Electric distribution utilities are required to continuously deliver reliable electric power to their customers. Regulatory utility commissions often practise reward and penalty schemes to regulate reliability performance of utility companies annually with respect to a desired performance targets. However, the conventional regulation procedures are commonly found based on the customer-based standard reliability indices, which are not able to discern the service characteristics behind the electric meters and, hence, fail to holistically characterise the actual impact of electricity interruption. This study proposes a new method to evaluate the load-based reliability indices in power distribution systems using advanced metering infrastructure data. Furthermore, the authors introduce a reward/penalty regulation scheme for utility regulators to provide a reliability oversight using the proposed load-based reliability metrics. The new load-based reliability metric and the reward/penalty scheme proposed bring about superior advantages as the distribution grids become further complex with a high penetration of distributed energy resources and enabled microgrid flexibilities. Numerical analyses on different settings with and without microgrid considerations reveal the applicability and effectiveness of the proposed approach in real-world scenarios.

DC–DC cascaded systems are vulnerable to instability and dynamic performance deterioration. These problems arise due to the nonideality in the source converter output impedance and to the negative input impedance of the load converter. Any overlap in magnitude between these two impedances will destabilize the voltage at the dc bus. Thus, we introduce a method to diminish the magnitude of source-converter output impedance such that the source converter will act as an ideal voltage source. As a consequence, the dynamic performance of the source and load converters will be intact, leading to ensuring the bus voltage stability. In addition, the controller is independent of the number of converters that form the load subsystem. To validate the proposed method, an experimental cascaded system was analyzed, simulated, and experimentally tested. Collectively, all of the outcomes proved the effectiveness of the proposed method.

A practical mitigation of the geomagnetic disturbance (GMD) consequences in power systems is not possible without taking into account the equipment thermal limits and operational constraints. This paper presents such limitations, develops the required equations and characteristics, and proposes a general and comprehensive approach to incorporate these limits in any short-term or long-term geomagnetically induced current (GIC) mitigating solution. As an application, this study formulates the limits in the context of an optimal placement of the GIC blocking devices at the neutral point of the power transformers in the IEEE 118-bus benchmark study system. The proposed optimization problem takes into account synchronous generator real/reactive power capability, acceptable bus voltage magnitudes, transformer hot-spot heating, transmission-line thermal limits, capacitor bank harmonic loading, and synchronous generator rotor heating. This paper shows that the existing standards significantly underestimate the generator rotor heating under the GMD conditions and propose more accurate alternatives. The study results show that considering the equipment thermal limits results in a noticeably different solution when compared with the case of ignoring such limits.

A multi-stage model predictive control approach is proposed to compensate the forecast error in a scenario-based two-stage stochastic dynamic economic dispatch problem through a feedback mechanism. Reformulating the problem as a finite moving-horizon optimal control problem, the proposed approach decelerates the growth of the number of scenarios by updating the system as uncertainties are gradually realized. Consequently, the computation time is reduced, and the problem is solved without the need for using scenario reduction techniques that compromise the accuracy of the solution. To exhibit the computational efficiency of the proposed approach, numerical experiments are conducted on the IEEE 118-bus system.

This study presents a control strategy for a microgrid that consists of multiple distributed generators (DGs), for both grid-connected and islanded modes of operation, where every DG is interfaced to the main grid via a voltage sourced inverter (VSI). finite control set model predictive control (FCS-MPC) is used as the primary controller to regulate the output power of each DG (in the grid-connected mode) or the voltage of the point of DG coupling (in the islanded mode of operation). In the grid-connected mode, direct power model predictive control (DPMPC) is implemented to manage the power flow between each DG and the main grid. In the islanded mode, voltage model predictive control (VMPC), as the primary control, and droop control, as the secondary control, are employed to control the output voltage of each DG and system frequency. The controller is equipped with a supplementary current limiting technique to limit the output current of each DG in case of overcurrent scenarios. The control approach also enables a smooth transition between the two modes. The performance of the control strategy is investigated and verified using PSCAD/EMTDC software platform.

Performance requirements and/or operational integrity of a microgrid subject to major changes, e.g., transition from a grid-connected to an islanded condition, transfer switch operation within the microgrid, or control communication failure, can necessitate the controllers (or a subset of them) to change their modes of operation, e.g., P/Q mode to V/f mode. Transfer in the control mode can further exacerbate the transients due to the original disturbance. This paper proposes mechanisms for smooth transition between control modes. Emphasis of the reported work is on control mode change subsequent to (1) grid-connected to islanded condition and (2) control communication failure. The proposed mechanisms are based on (1) an observer (or state estimator) and (2) an auxiliary tracking controller. The effectiveness of the proposed mechanisms are investigated based on digital time-domain studies in the PSCAD/EMTDC environment and verified in an RTDS-based hardware-in-the-loop platform.

The optimal-power-flow-based problems, such as economic dispatch, unit commitment, optimal power flow, market clearing, and power system expansion planning, are subject to various uncertainties which include, but are not limited to, demand fluctuation, generation/transmission outages, adverse weather conditions, and electricity pricing. The large integration of renewable energy resources, such as wind and solar, has further caused additional uncertainties due to the variable and unpredictable nature of these resources. However, the next generation smart power systems are also equipped with enabling technologies such as control, communication, and powerful computing capabilities that could be utilized to better deal with these uncertainties and operate the power system at a stable, reliable, and economic operating point. Stochastic optimization is a powerful tool that enables power system operators to deal with such uncertainties. This paper provides a technical overview of recent advancements in this area and further provides an insight on the treatment of next-generation power systems considering the involved high level of uncertainty.

Grid synchronization is an important part in monitoring, accessing, and automatically controlling the grid-connected generators and other power electronic converters. The phase of fundamental frequency needs to be tracked in real-time to control energy delivery. However during normal operation the waveforms’ frequency, magnitude, and symmetry are varying due to load change, distortions generated by certain electrical power devices and the occurrence of faults. In this paper, an efficient and fast synchronization scheme based on Delayed Signal Cancellation (DSC) for harmonics rejection purposes has been proposed for extracting the fundamental signal. Also, a detailed α β -frame Cascade DSC ( α β CDSC) analysis is presented and an α β CDSC-PLL scheme with fast dynamic response under transients is proposed. Based on the proposed PLL, an adaptive control loop is also developed to improve disturbance resistance during faults. Both simulation and real-time hardware-in-the-loop (HIL) experiments are used to verify the unbalance resistance, distortion rejection as well as performance improvement of the two proposed α β CDSC-PLL schemes.

In this paper, we introduce a method to eliminate the impact of constant power loads (CPLs) on dc distributed systems. A CPL, in small-signal sense, resembles a negative resistance that is connected to its source. The negative resistance interacts with the source output impedance, leading to instability or degrading the dynamic performance at the dc-bus. The proposed method injects the bus-voltage oscillations into the control loop of the source-converter using a high-pass filter. The filter suppresses the dc quantity of the bus-voltage, while passing the high-frequency oscillations. Thus, the operating point of the source-converter is not modified, while the dynamic performance is improved. A prototype dc distributed system was analytically analyzed, simulated, and experimentally tested in order to validate the effectiveness of the proposed controller.

This paper presents a method to stabilize DC power distributed systems (DPSs) under Average-Current-Mode (ACM) control technique. ACMs lacked the attention as the Voltage-Mode-Control (VMC) and Peak-Current-Mode (PCM) control methods are prevalent in the literature. The proposed method adds an extra negative feedback to the original ACM controller in order to eliminate the impedance interaction between the source and load converters. As a result, the peaking in the source-converter output impedance (Z o ) was eliminated. From a mathematical perspective, the added feedback loop compensates for the magnitude deficiency, which occurs at the resonance frequency of the source-converter, in the denominator of Z o . Hence, the dipping in the denominator magnitude was removed. The proposed controller was studied and analyzed mathematically, by simulations, and experimentally. All of the outcomes validated the effectiveness of the proposed controller.

This paper proposes a method to stabilize and enhance the dynamic performance of a cascaded DC-DC system by adaptively reshaping the source output impedance. The method aims to reduce the ratio of the source output impedance to the load input impedance, referred to as the minor loop gain, to eliminate the interaction between the load and the source systems. This interaction can deteriorate the dynamic performance or might lead to instability. Thus, the bus current is used to improve the dynamic performance by reducing the magnitude of the source’s output impedance adaptively according to the loading condition such that the dynamic performance is consistently improved. Utilizing the bus current facilitates the compatibility between the proposed controller and most widely used DC-DC converters controlled in voltage mode, including non-minimum phase converters. In addition to the flexibility the bus current provides to embed the proposed solution with conventional control schemes. Experimental results have validated the effectiveness of the proposed controller along with time-based simulation and theoretical analysis, for minimum and non-minimum phase converters.

This paper presents a method to eliminate the sensitivity of impedance-interaction between source and load converters in a DC distributed power system (DPS). The impedance-interaction adversely alters the dynamic performance of the switched-mode converters, in a DPS, leading to destabilizing the voltage at their point of interface. Therefore, the proposed method lowers the interaction level by shaping the magnitude of the source converter output impedance (Z o (s)). Reducing the magnitude of Z o (s) mitigates the impedance-interaction and, consequently, improves the dynamic performance of each converter. The proposed controller utilizes the output voltage (V o ) of the source converter in order to reduce |Z o (s)| by a factor of (1 + V o ). The controller is effective with output voltages as low as 1 V because 1 V will reduce |Z o (s)| by 6 dB. The controller was experimentally validated in order to prove the offered theoretical analyses and time-based simulations.

Microgrids consisting of multiple distributed energy resources (DERs) provide a promising solution to integrate renewable energies, e.g., solar photovoltaic (PV) systems. Hybrid AC/DC microgrids leverage the merits of both AC and DC power systems. In this paper, a control strategy for islanded multi-bus hybrid microgrids is proposed based on the Finite-Control-Set Model Predictive Control (FCS-MPC) technologies. The control loops are expedited by predicting the future states and determining the optimal control action before switching signals are sent. The proposed algorithm eliminates the needs of PI, PWM, and droop components, and offers 1) accurate PV maximum power point tracking (MPPT) and battery charging/discharging control, 2) DC and multiple AC bus voltage/frequency regulation, 3) a precise power sharing scheme among DERs without voltage or frequency deviation, and 4) a unified MPC design flow for hybrid microgrids. Multiple case studies are carried out, which verify the satisfactory performance of the proposed method., Comment: This paper has been accepted by the 2018 IEEE PES General Meeting

This paper proposes a control and power sharing strategy for small-scale microgrids in both grid-connected and islanded modes based on the centralized Finite Control Set Model Predictive Control (FCS-MPC). In grid-connected mode, the controller is capable of managing the output power of each Distributed Generator (DG) and enables flexible power regulation between the microgrid and the utility grid. In islanded mode, the controller regulates the microgrid voltage and frequency, and provides a precise power sharing scheme among DGs. In addition, the power sharing can be adjusted flexibly by changing the sharing ratio. The proposed control also enables plug-and-play operation. Moreover, a smooth transition between the two modes of operation is achieved without any disturbance in the system. Case studies are carried out to verify the proposed control strategy with the PSCAD/EMTDA software package.

DC distributed systems are highly reliable and efficient means of delivering DC power or adopting renewable energy resources. However, DC distributed systems are prone to instability and dynamic performance degradation due to the negative incremental input impedance of DC-DC converts. In this paper, we propose a generic method to eliminate the impact of the negative input impedance on DC systems by shaping the source output impedance such that its bode-plot is restricted in the area that is contained below the product of the source’s duty ratio and its characteristic impedance. The performance deterioration originates whenever the output impedance of the source exceeds, in magnitude, the input impedance of the load converter due to deficiency in stability margins. Hence, confining the impedance in the proposed region helps decouple the interaction between the converters and preserve their own dynamic performances. The proposed method was proven by analytical analysis, time-based simulation, and practical experiments. All of their outcomes were in agreement, proving the effectiveness of the proposed method in preserving the dynamic performance of distributed systems.

This paper presents an optimization approach for the placement of geomagnetically induced current (GIC) blocking devices (BDs) on power system transformers to mitigate the adverse effects of a geomagnetic disturbance (GMD). Solar storms lead to GMDs which, in turn, drive GICs along transmission lines and through transformer windings. GICs cause half-cycle saturation in power transformers, increase their shunt reactive power loss, lead to a significant lack of power system reactive power support, and, as a result, create voltage instability and, potentially, large-scale voltage collapse. To mitigate these detrimental effects, a long-term remedy is to install GIC BDs at the neutral point of transformers. Since these devices are fairly costly, their placement should be performed in an optimal manner. The optimization problem proposed in this paper minimizes the cost of BD placement while satisfying power system voltage and generator maximum reactive power limits. The numerical results demonstrate the effectiveness of the proposed method in maintaining an acceptable voltage profile for the power system in case of GMDs with any degree of severity.

This paper presents the fundamental concepts of a generalized central power management system and a decentralized, robust control strategy for autonomous mode of operation of a microgrid that includes multiple distributed energy resource (DER) units. DER units are divided into voltage-controlled and power-controlled DER units. The frequency of each DER unit is determined by its independent internal oscillator and all oscillators are synchronized by a common time-reference signal based on a global positioning system (GPS). The power management system (PMS) specifies the power and voltage set points for the local controllers of each DER unit. A linear, time-invariant, multivariable, robust, decentralized control system is designed to track the setpoints. Each control agent guarantees fast tracking, zero steady state error, and robust performance despite uncertainties of the microgrid parameters, topology, and the operating point. Existence conditions, control design procedures, eigenanalysis and robust stability analysis of the closed-loop system, and performance of the control strategy based on digital time-domain simulation studies in PSCAD/EMTDC platform, are reported. Performance of the control system is also verified based on hardware-in-the-loop (HIL) studies in the RTDS environment.

Line-to-Line (L-L) faults in Photovoltaic (PV) arrays prevent the PV system from producing maximum power, and if not cleared, may result in serious energy and revenue losses and cause fire hazards. Maximum Power Point Tracking (MPPT), a technique employed to maximize the power output of a PV array at different irradiance level, may potentially mask certain faults and make them undetectable by protection devices, especially when these faults occur under low solar irradiance condition or with high fault impedance. This paper proposes an innovative algorithm for detecting L-L faults in PV arrays based on support vector machine (SVM).

In Part I of this two-part paper, a power-management and a control strategy for the microgrid autonomous mode of operation were presented. The strategy consists of 1) open-loop frequency control of the system and synchronization of DER units based on a GPS signal; 2) voltage reference setpoint determination for the DER units by the central power-management system; and 3) tracking the assigned setpoints and rejecting disturbances by robust, decentralized, local controllers of DER units. This Part II paper applies the envisioned strategy to a three-DER microgrid. Offline digital time-domain simulation studies in the EMTDC/PSCAD software environment demonstrate the robustness of the local controllers to parametric, topological, and unmodelled uncertainties of the microgrid, its fast performance in tracking the setpoints with zero steady-state error, and rapid disturbance rejection. The results also show the effectiveness of the proposed power-management system in achieving prescribed load sharing of DER units. The digitized algorithms of the proposed control system of the three-DER microgrid are also implemented in NI-cRIO industrial-grade platforms and tested in an RTDS-based real-time hardware-in-the-loop (HIL) environment to demonstrate the feasibility of the strategy for hardware implementation and hardware-based performance validation.

This paper approaches the topic of protection system reliability from an economic point of view by 1) designing an optimal modern protective relay based on a prescribed level of reliability subject to economic constraints and 2) determining optimal routine test intervals by balancing the cost of routine tests and losses due to relay failure. This paper proposes a method that enables the designer to optimally select hardware and software components of a digital protective relay to gain the highest possible overall reliability with a restricted budget. This design can be viewed as a reliability and redundancy allocation problem for which a new easy-to-implement algorithm is proposed. In contrast to the existing literature that views the problem of routine test interval optimization solely from a reliability viewpoint, this paper proposes an optimization procedure for determining optimal test intervals. The optimization objective function consists of three terms: cost of routine tests, losses due to relay unresponsiveness, and losses due to relay maloperation. The value of the objective function is calculated based on a new, simple, and comprehensive Markov model. Illustrative numerical examples clarify the application of the proposed methods and demonstrate their effectiveness in achieving an optimum for both cases.

Fixed gain controllers for automatic generation control are designed at nominal operating conditions and fail to provide best control performance over a wide range of operating conditions. So, to keep system performance near its optimum, it is desirable to track the operating conditions and use updated parameters to compute control gains. A control scheme based on artificial neuro-fuzzy inference system (ANFIS), which is trained by the results of off-line studies obtained using particle swarm optimization, is proposed in this paper to optimize and update control gains in real-time according to load variations. Also, frequency relaxation is implemented using ANFIS. The efficiency of the proposed method is demonstrated via simulations. Compliance of the proposed method with NERC control performance standard is verified.

The protection system plays a vital role in maintaining a high degree of service reliability in present-day power systems. Considerable work has been done to examine different aspects of protection systems and their associated reliability. All of the reliability predictive methods are based solely on equipment failures, neglecting the human component of the protection system. In this paper, a modified Markov model is presented that incorporates human errors in reliability evaluation of protective devices. Two main types of relay failure (failure to operate and inadvertent operation) are taken into account in the proposed model. The model also considers routine test inspections, the self-checking facility of digital relays, and also common cause failures of protective devices and protected components. Numerical studies show the influence of human errors on protection system reliability.

This paper presents a methodology for optimizing investment in data center battery storage capacity. Utility grid managers spend significant resources towards predicting and matching available power generation capacity to demand in real time. It is therefore essential for the success of the power industry that economic dispatch, energy efficiency, and grid security be maintained as power requirements change. This is especially challenging for microgrids during periods of peak demand due to limited available capacity. Data centers possess a unique requirement for short-term battery power supply where cost savings, emissions reduction, and reliability enhancement can be achieved through investment in additional battery capacity. To maximize these benefits, an optimization methodology is presented through a case study for an existing data center and microgrid. Here we discuss a case study demonstrating the effectiveness of the proposed approach. For the selected mid-size data center our results indicate monetize monthly savings of up to ten thousand dollars and 0.5 percent reduction in loss of load probability while simultaneously reducing carbon footprints. The results of this work are directed towards large data centers at university and corporate campuses, microgrids, and military installations.

The increasing interest in integrating intermittent renewable energy sources into microgrids presents major challenges from the viewpoints of reliable operation and control. In this paper, the major issues and challenges in microgrid control are discussed, and a review of state-of-the-art control strategies and trends is presented; a general overview of the main control principles (e.g., droop control, model predictive control, multi-agent systems) is also included. The paper classifies microgrid control strategies into three levels: primary, secondary, and tertiary, where primary and secondary levels are associated with the operation of the microgrid itself, and tertiary level pertains to the coordinated operation of the microgrid and the host grid. Each control level is discussed in detail in view of the relevant existing technical literature.

This paper presents the results of an eigenvalue and robustness analysis of a multi-DER islanded microgrid voltage control scheme. The configuration, parameters, and linear time-invariant (LTI) state-space model of the microgrid are provided. The control strategy, control parameters, and the LTI state-space model of the closed-loop system are also given. An eigenvalue analysis is performed to investigate the dynamic behavior of the study system as microgrid electrical parameters and control gains change. A robustness analysis of the control strategy using the notion of real stability radius is carried out and results are presented. A PSCAD/EMTDC simulation case study is carried out to verify the results of eigenvalue/robustness analysis.

This paper presents the challenges that the power converters with $dq$ -frame-based control confront when subjected to ferroresonance. This is mainly due to two main properties of the widely used $dq$ -frame control systems. It is demonstrated in this paper that regardless of the phase-to-ground or phase-to-phase voltage measurement, the $dq$ -frame control only responds to phase-to-phase voltage variations. Furthermore, the PI controllers, which are employed in conjunction with the $dq$ reference frame, can inherently only track dc references and reject dc disturbances. Consequently, a $dq$ -based PI controller can only respond to the positive-sequence phase-to-phase voltage variations, effectively. This, in turn, limits the disturbance mitigation capability of the power converter. This study investigates the impacts of the ferroresonance phenomenon on the control system response and the operating conditions of the power converters. Based on a droop-based $dq$ frame controller, the behavior of an electronically interfaced distribution generation system is studied, under various transient conditions. In spite of a promising performance under the load change and islanding scenarios, the $dq$ -frame-based controller of the power converter fails to detect and respond to the voltage fluctuation and excessive overvoltages as a result of ferroresonance.

High-impedance faults (HIFs) on distribution systems create unique challenges to protection engineers. HIFs do not produce enough fault current to be detected by conventional overcurrent relays or fuses. A method for HIF detection based on the nonlinear behaviour of current waveforms is presented. Using this method, HIFs can be distinguished successfully from other similar waveforms such as nonlinear load currents, secondary current of saturated current transformers and inrush currents. A wavelet multi-resolution signal decomposition method is used for feature extraction. Extracted features are fed to an adaptive neural fuzzy inference system (ANFIS) for identification and classification. The effect of choice of mother wavelet is also analysed by investigating a large number of wavelet families. Various simulation results, which are obtained using an appropriate model, are summarised and efficiency of the proposed algorithm for dependable and secure HIF detection is determined.

Failure statistics of most utilities indicate that distribution systems make the greatest individual contribution to the unavailability of supply to customers. Optimal capacitor placement in distribution systems has a number of advantages such as reducing losses, improving voltage profile, improving power factor and so on. The conventional objective function of the optimal capacitor placement consists of the total cost of losses and investments. Since capacitors supply reactive loads locally, they improve the load-carrying capability of the lines and therefore play the same role as redundant lines. Thus, optimal capacitor placement can also improve the reliability indices of a distribution system. Therefore two new objective functions are defined here. The first one is defined as the sum of reliability cost and investment cost. The second is defined by adding the reliability cost, cost of losses and investment cost. The latter is a comprehensive objective function which is the resultant of the conventional and reliability-based objective functions. This problem is solved using a particle swarm optimisation-based algorithm. The effectiveness and applicability of the proposed approach are examined using a distribution network.