The Internet of Things (IoT) is a technology that allows every object or item to become part of the Internet and interact with each other. One of the technologies based on the IoT is Long Range (LoRa). Apart from the increasing number of IoT services, security aspects become a separate issue in the development of the IoT. One of the solutions is to utilize blockchain technology in the IoT topology to secure the data and transactions that occur in the IoT network. The blockchain can take minutes to compute a cryptographic chain. It also needs sufficient computing resources. This problem gave rise to the idea of establishing a lightweight blockchain platform with low latency that could run on devices with low computing resources as well as IoT devices. We offered a technology called Lightweight Multi-Fog (LMF) in our previous publication that is implemented using the Lightweight Scalable Blockchain (LSB) algorithm and the fog network on the IoT to solve the problem of integrating a block... [more]

The Energy Internet paradigm is the evolution of the Internet of Things concept in the power system. Microgrids (MGs), as the essential element in an Energy Internet, are expected to be controlled in a corporative and flexible manner. This paper proposes a novel decentralized robust control strategy for multi-agent systems (MASs) governed MGs in future Energy Internet. The proposed controller is based on a consensus algorithm applied with the connected distributed generators (DGs) in the MGs in the energy internet paradigm. The proposed controller’s objectives are the frequency/voltage regulation and proportional reactive/active power-sharing for the hybrid DGs connected MGs. A proposed two-level communication system is implemented to explain the data exchange between the MG system and the cloud server. The local communication level utilizes the transmission control protocol (TCP)/ internet protocol (IP) and the message queuing telemetry transport (MQTT) is used as the protocol for the... [more]

In this paper, we present a detailed analysis of walkaway vertical seismic profiling (VSP) data, which can be used to obtain Thomsen parameters using P-wave-only inversion. Data acquisition took place in difficult field conditions, which influenced the quality of the data. Therefore, this paper also shows a seismic data processing scheme that allows the estimation of correct polarization angles despite poor input data quality. Moreover, we showed that it is possible to obtain reliable and detailed values of Thomsen’s anisotropy parameters for data that are challenging due to extremely difficult field conditions during acquisition and the presence of an overburden of salt and anhydrite (Zechstein formation). This complex is known for its strong seismic signal-attenuating properties. We designed a special processing workflow with a signal-matching procedure that allows reliable estimation of polarization angles for low-quality data. Additionally, we showed that P-wave-only inversion for... [more]

Recent research advances in sensors, wireless communications, network protocols, microelectronics, cloud computing, and machine learning, among others, are driving the growth of the Internet of Things (IoT) [...]

Physical activity involves movements, which can be considered sources of kinetic energy, that are expected to be important during sports activities. Several transducers can transform this energy into electrical energy. Piezoelectric generators are widely used, and several applications highlight their relevance. However, the generated output power is location dependent, and the analysis of the placement of this kind of generator can be challenging. In order to assess the availability of kinetic energy sources, an acceleration data analysis method is presented. Temporal and harvester model-based studies, using data from 17 inertial measurement units (IMUs) located across the whole human body, were conducted. The results show that piezoelectric cantilever-beam harvesters can be very sensitive to impacts. Extremity segments, such as the feet or hands, can be considered as good energy sources. The most relevant features are proposed as criteria to easily evaluate the harvestable energy sour... [more]

In the present day, it is crucial for individuals and companies to reduce their carbon footprints in a society more self-conscious about climate change and other environmental issues. In this sense, public and private institutions are investing in photovoltaic (PV) systems to produce clean energy for self-consumption. Nevertheless, an essential part of this energy is wasted due to lower consumption during non-business periods. This work proposes a novel framework that uses solar-generated energy surplus to charge external electric vehicles (EVs), creating new business opportunities. Furthermore, this paper introduces a novel marketplace platform based on blockchain technology to allow energy trading between institutions and EV owners. Since the energy provided to charge the EV comes from distributed PV generation, the energy’s selling price can be more attractive than the one offered by the retailers—meaning economic gains for the institutions and savings for the users. A case study wa... [more]

Several factors affect existing electric power systems and negatively impact power quality (PQ): the high penetration of renewable and distributed sources that are based on power converters with or without energy storage, non-linear and unbalanced loads, and the deployment of electric vehicles. In addition, the power grid needs more improvement in the performances of real-time PQ monitoring, fault diagnosis, information technology, and advanced control and communication techniques. To overcome these challenges, it is imperative to re-evaluate power quality and requirements to build a smart, self-healing power grid. This will enable early detection of power system disturbances, maximize productivity, and minimize power system downtime. This paper provides an overview of the state-of-the-art signal processing- (SP) and pattern recognition-based power quality disturbances (PQDs) characterization techniques for monitoring purposes.

The nuclear industry is exploring applications of Artificial Intelligence (AI), including autonomous control and management of reactors and components. A condition assessment framework that utilizes AI and sensor data is an important part of such an autonomous control system. A nuclear power plant has various structures, systems, and components (SSCs) such as piping-equipment that carries coolant to the reactor. Piping systems can degrade over time because of flow-accelerated corrosion and erosion. Any cracks and leakages can cause loss of coolant accident (LOCA). The current industry standards for conducting maintenance of vital SSCs can be time and cost-intensive. AI can play a greater role in the condition assessment and can be extended to recognize concrete degradation (chloride-induced damage and alkali−silica reaction) before cracks develop. This paper reviews developments in condition assessment and AI applications of structural and mechanical systems. The applicability of exist... [more]

With the realization of the “dual carbon” goal, urban public transport with an increasing proportion of new energy vehicles will become the key subject to achieve the carbon emission reduction goal. Under the new background of deep coupling between transport networks and power grids, it is of great significance to study the carbon-trading mode of urban public transport participation in promoting the development of new energy vehicles and improving the operating efficiency and low-carbon level of the “energy-transport” system. In this paper, based on blockchain technology, a framework for urban public transportation networks to participate in carbon trading is established to solve the current problems of urban public transportation’s insufficient motivation to reduce emissions, lax operation strategy and lack of carbon-trading matching mechanisms. Finally, Hyperledger Fabric was selected as the simulation platform, and we simulated the model through the calculation example. The results... [more]

Constantly growing distributed energy generation based on renewable sources creates a number of new challenges for electrical power system operation. One of the challenges is islanding detection. Unintentional islanding, which can cause health and safety hazards for the personnel, is currently being experienced by a growing number of consumers/prosumers especially in the case of photovoltaic inverters. This work presents a new islanding detection method based on synchrophasor measurements. The proposed method works in either a passive or hybrid mode. In a passive mode, a single phasor measurement unit (PMU) in the island region is used. In a hybrid mode, one PMU in the island and another one outside the island are exploited. The proposed method was verified in conducted laboratory tests that confirmed the applicability of PMUs data for effective detection and monitoring of unintentional islanding.

Demand response (DR) services have the potential to enable large penetration of renewable energy by adjusting load consumption, thus providing balancing support to the grid. The success of such load flexibility provided by industry, communities, or prosumers and its integration in electricity markets, will depend on a redesign and adaptation of the current interactions between participants. New challenges are, however, bound to appear with the large scale contribution of smaller assets to flexibility, including, among others, the dispatch coordination, the validation of delivery of the DR provision, and the corresponding settlement of contracts, while assuring secured data access among interested parties. In this study we applied distributed ledger (DLT)/blockchain technology to securely track DR provision, focusing on the validation aspect, assuring data integrity, origin, fast registry, and sharing within a permissioned system, between all relevant parties (including transmission sys... [more]

Blockchain technology is currently one of the trends considered to have a tremendous future ahead. It ensures data security, data sharing protection and automation development—elements that are of colossal importance in the era of cloud solutions, big data and Internet of Things (IoT) reality. Additionally, blockchain technology allows one to create new programmable ecosystems on an unprecedented scale. The implementation of blockchain technology leads not only to improving the flow of documents and data storage, as is the case with the creation of shared service centers (SSCs), but—as this paper shows—allows one to reduce the carbon footprint when servicing SSCs at a considerably higher organizational level at the same time. The example of an SSC in Elbląg, Poland, proves that cloud solutions enabling electronic documents flow and data storage combined with blockchain technology are tools essential for further SSCs development. Furthermore, such tools allow us not only to obtain econo... [more]

In this paper, the enhanced auto-tuning technique based on the injection of two sinusoidal test signals of different frequencies applicable on the low voltage induction motor self-commissioning process is presented. The main feature of the proposed technique resides in the advanced signal processing of measured IM voltage and current signals based on the cascaded delay signal cancelation structure. This processing algorithm enables the filtering of the symmetry-related fundamental harmonic from the non-symmetrical test signal excitation typical for the self-commissioning process. Based upon the steady-state response from the proposed filtering block, the simple yet effective calculation method derives the complete parameter set of the IM equivalent circuit. The technique is validated through the variety of computer simulations and experimental tests on the digitally controlled low voltage IM traction drive.

We present a suitable approach to address the electric vehicle charging roaming problem (e-roaming). Blockchain technologies are applied to support the identity management process of users charging their vehicles and to record energy transactions securely. At the same time, off-chain cloud-based storage is used to record the transaction details. A user wallet settled on a mobile application stores user verified credentials; a backend application in the vehicle charging station validates the user credentials to authorize the energy transaction. The current model can be applied to similar contexts where the user may be required to keep several credentials from different providers to authenticate digital transactions.

Symplectic geometric mode decomposition (SGMD) is a newly proposed signal processing method. Because of its superiority, it has gained more and more attention in the field of fault diagnosis. However, the similar component reorganization problem involved in this method has not been clearly stated. Aiming at this problem, this paper proposes the SGMD-CS method based on the SGMD method and the cosine similarity (CS) and has been compared and verified on the simulation signal and the actual rolling bearing signal. In addition, in order to realize the intelligent diagnosis of the wind turbine bearing fault, the symplectic geometric entropy (SymEn) is extracted as the fault feature and input it into the AdaBoost classification model. In summary, this paper proposes a new wind turbine fault feature extraction method based on the SGMD-CS and AdaBoost framework, and the validity of the method is verified by the rolling bearing vibration data of the Electrical Engineering Laboratory of Case Wes... [more]

In this paper, novel methods for the evaluation of digital-filter stability are investigated. The methods are based on phase analysis of a complex function in the characteristic equation of a digital filter. It allows for evaluating stability when a characteristic equation is not based on a polynomial. The operation of these methods relies on sampling the unit circle on the complex plane and extracting the phase quadrant of a function value for each sample. By calculating function-phase quadrants, regions in the immediate vicinity of unstable roots (i.e., zeros), called candidate regions, are determined. In these regions, both real and imaginary parts of complex-function values change signs. Then, the candidate regions are explored. When the sizes of the candidate regions are reduced below an assumed accuracy, then filter instability is verified with the use of discrete Cauchy’s argument principle. Three different algorithms of the unit-circle sampling are benchmarked, i.e., global com... [more]

The fault diagnosis of electrical machines during startup transients has received increasing attention regarding the possibility of detecting faults early. Induction motors are no exception, and motor current signature analysis has become one of the most popular techniques for determining the condition of various motor components. However, in the case of inverter powered systems, the condition of a motor is difficult to determine from the stator current because fault signatures could overlap with other signatures produced by the inverter, low-slip operation, load oscillations, and other non-stationary conditions. This paper presents a speed signature analysis methodology for a reliable broken rotor bar diagnosis in inverter-fed induction motors. The proposed fault detection is based on tracking the speed fault signature in the time-frequency domain. As a result, different fault severity levels and load oscillations can be identified. The promising results show that this technique can b... [more]

The application of Internet of Things (IoT) has been emerging as a new platform in wireless technologies primarily in the field of designing electric vehicles. To overcome all issues in existing vehicles and for protecting the environment, electric vehicles should be introduced by integrating an intellectual device called sensor all over the body of electric vehicle with less cost. Therefore, this article confers the need and importance of introducing electric vehicles with IoT based technology which monitors the battery life of electric vehicles. Since the electric vehicles are implemented with internet, an online monitoring system which is called Things Speak has been used for monitoring all the vehicles in a continuous manner (day-by-day). These online results will then be visualized in MATLAB after an effective boosting algorithm is integrated with objective function. The efficiency of proposed method is tested by visual analysis and performance results prove that the projected met... [more]

In the electricity market, different pricing models can be applied to increase market competitiveness. Different electricity systems use different market structures. Uniform marginal pricing, zonal marginal pricing, and nodal marginal pricing methods are commonly used market structures. For markets wishing to move from a uniform pricing structure to a more competitive zonal pricing structure, the determination of price zones is critical for achieving a competitive market that generates accurate price signals. Three different pricing zone detection algorithms are analyzed in this paper including the k-means clustering and queen/rook spatially constraint clustering. Finally, the results of a case study for the Turkish electricity system are shared to compare each method.

In this paper, a device with swinging plate electrodes has been proposed to measure contact electrification of a liquid sample. The proposed structure is composed of two parallel metallic plates immersed in a dielectric liquid. One of the plates is swinging with a constant frequency in a range from 0.4 to 4 Hz. The paper investigates the dependence in time and frequency of electrode velocity and streaming electrification. The measured current occurs for a very low intermittent velocity of less than 10 mm/s. In this range, the electrification current is around 50 pA. For higher velocities of up to 150 mm/s, the current is at the level of 1200 pA. The time−frequency characteristic using short-time Fourier transform shows no temporal changes in the frequency spectrum. The dependence of electrification on shuttle speed was calculated and it can be approximated with a second order polynomial model with the determination coefficient higher than 0.9. The advantage of the sensor is the ability... [more]

For the next generation of manufacturing, the industrial internet of things (IoT) has been considered as a key technology that enables smart factories, in which sensors transfer measured data, actuators are controlled, and systems are connected wirelessly. In particular, the wireless sensor network (WSN) needs to operate with low cost, low power (energy), and narrow spectrum, which are the most technical challenges for industrial IoT networks. In general, a relay-assisted communication network has been known to overcome scarce energy problems, and a spectrum-sharing technique has been considered as a promising technique for the radio spectrum shortage problem. In this paper, we propose a phase steering based hybrid cooperative relaying (PSHCR) technique for the generic relay-assisted spectrum-shared WSN, which consists of a secondary transmitter, multiple secondary relays (SRs), a secondary access point, and multiple primary access points. Basically, SRs in the proposed PSHCR technique... [more]

Solar energy is the most promising renewable resource with an unbounded energy source, capable of meeting all human energy requirements. Solar Photovoltaic (SPV) is an effective approach to convert sunlight into electricity, and it has a promising future with consistently rising energy demand. In this work, we propose a smart solution of outdoor performance characterization of the SPV module utilizing a robust, lightweight, portable, and economical Outdoor Test Facility (OTF) with the Internet of Things (IoT) capability. This approach is focused on the capacitive load-based method, which offers improved accuracy and cost-effective data logging using Raspberry Pi and enables the OTF to sweep during the characterization of the SPV module automatically. A demonstration using an experimental setup is also provided in the paper to validate the proposed OTF. This paper further discusses the advantages of using the capacitive load approach over the resistive load approach. IoT’s inherent bene... [more]

This paper, for the first time, investigates the possibility of exploiting a nonlinear bistable snap-through buckling structure employing piezoelectric transducers, to implement an autonomous sensor of mechanical vibrations, with an embedded energy harvesting functionality. The device is operated in the presence of noisy vibrations superimposed on a subthreshold deterministic (sinusoidal) input signal. While the capability of the device to harvest a significant amount of energy has been demonstrated in previous works, here, we focus on the signal processing methodology aimed to extract from the sensor output the information about the noise level (in terms of the standard deviation) and the root mean square amplitude of the deterministic component. The developed methodology, supported by experimental evidence, removes the contribution to the overall piezoelectric output voltage ascribable to the deterministic component using a thresholding and windowing algorithm. The contribution to th... [more]

Current Peer-to-Peer (P2P) energy market models raise serious concerns regarding the confidentiality and integrity of energy consumption, trading and billing data. While Distributed Ledger Technology (DLT) systems (e.g., blockchain) have been proposed to enhance security, an attacker could damage other parts of the model, such as its infrastructure: an adversarial attacker could target the communication between entities by, e.g., eavesdropping or modifying data. The main goal of this paper is to propose a model for a decentralised P2P marketplace for trading energy, which addresses the problem of developing security and privacy-aware environments. Additionally, a Multi-Agent System (MAS) architecture is presented with a focus on security and sustainability. In order to propose a solution to DLT’s scalability issues (i.e., through transaction confirmation delays), off-chain state channels are considered for the energy negotiation and resolution processes. Additionally, a STRIDE (spoofin... [more]

In recent years, machine learning applications have received increasing interest from power system researchers. The successful performance of these applications is dependent on the availability of extensive and diverse datasets for the training and validation of machine learning frameworks. However, power systems operate at quasi-steady-state conditions for most of the time, and the measurements corresponding to these states provide limited novel knowledge for the development of machine learning applications. In this paper, a data mining approach based on optimization techniques is proposed for filtering root-mean-square (RMS) voltage profiles and identifying unusual measurements within triggerless power quality datasets. Then, datasets with equal representation between event and non-event observations are created so that machine learning algorithms can extract useful insights from the rare but important event observations. The proposed framework is demonstrated and validated with both... [more]